Impact of Physical Objects on Scaring of Geese – with an Agroecological Approach Towards the Issue of Geese As a Pest in Agriculture

Impact of Physical Objects on Scaring of Geese – With an agroecological approach towards the issue of geese as a pest in agriculture

Påverkan av fysiska objekt på skrämsel av gäss - Med ett agroekologiskt grepp på gäss som skadegörare i lantbruket

Elias Kvarnbäck

Independent project • 30 credits Swedish University of Agricultural Sciences, SLU Faculty of Landsacape Architecture, Horticulture and Crop Production Sience Agroecology – Master’s programme Alnarp, 2021

Impact of Physical Object on Scaring of geese – With an agroecological approach towards the issue of geese as a pest in agriculture Påverkan av fysiska objekt på skrämsel av gäss – Med ett agroekologiskt grepp på gäss som skadegörare i lantbruket

Elias Kvarnbäck

Supervisor: Johan Månsson, SLU, Department of Ecology, Wildlife Ecology Unit Assistant supervisor: Johan Elmberg, Kristianstad University, Department of Environmental Science and Bioscience Examiner: Matias Jonsson, Swedish University of Agricultural Sciences, SLU, Department of Ecology

Credits: 30 credits Level: Advanced level, A2E Course title: Independent project in Agricultural Science, A2E – Agroecology – Master’s programme Course code: EX0848 Programme/education: Agroecology – Master’s programme Course coordinating dept: Department of Biosystems and Technology

Place of publication: Alnarp Year of publication: 2021

Keywords: Geese, barnacle goose, greylag goose, large grazing birds, agroecological practices, flight initiation distance

Swedish University of Agricultural Sciences Faculty of Landscape Architecture, Horticulture and Crop Production Science (LTV) Department of Biosystems and Technology

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Abstract The interactions and conflicts between geese and agricultural interests have risen in the last decades in Sweden. A range of measures are used by humans to disturb and scare geese with the goal to counteract crop damage. The hypothesis of this master thesis is that proximity to physical objects taller than 70 cm above the ground, e.g., woody perennials, houses or naturally occurring topographical features, makes greylag and barnacle geese easier to scare off crops. The incentive to inquire the effect of physical objects on scaring is that landscape and field features such as hedges, agroforestry and buffer strips are often suggested as agroecological practices. Presence of such element is relevant since geese tend to prefer to forage on fields with good visibility range. The data collected could however not prove that geese are more easily scared/disturbed as they are closer situated to physical objects. Among mixed flocks and greylag goose flocks, proximity to physical objects even made them harder to scare away from agricultural fields.

Keywords: Geese, barnacle goose, greylag goose, large grazing birds, agroecological practices, flight initiation distance

Preface

The broad scope of agroecology presents many opportunities for master thesis topics. Especially if one embraces the food system approach and not just the agroecosystem. I struggled for a while to choose my topic, but a basic yet very powerful sentence that made me finalize my choice, was the following (Smith & Smith 2015 p. 87):

How adaptations enable an organism to function in the prevailing environment – and conversely how those same adaptations limit its ability to function in other environment – is the key to understanding the distribution and abundance, the ultimate objective of the science of ecology.

During my time as a master student, I’ve sometimes felt that the approach in classical ecology differs a lot from the agroecological approach, where the latter is much more applied and value driven. The interactions between geese on agricultural land and humans, should therefore be seen from an applied perspective, if it is to be considered agroecology.

To write a master thesis, has been quite a different process and feeling than taking other university courses. Without my helpful and cheerful supervisors Johan Månsson and Johan Elmberg, I’d probably have felt lost and bored. The skills, that I’ve trained and come to value the most while writing my master thesis, are probably statistics in R as well as practical field work. Critical thinking and writing came naturally to me after 1.5 years of courses in agroecology.

Table of contents

1. Introduction ...... 10

Hypothesis and aim ...... 11 Study design ...... 12

2. Background ...... 13

Geese ...... 13 2.1.1. Taxonomy and species identification ...... 13 2.1.2. Physiology and ecology ...... 15 2.1.3. Population and damage trends ...... 16 Legislation...... 18 Diversification and agroecological practices ...... 19

3. Materials and methods ...... 20

Species, scaring sites and limitations ...... 21 Scaring procedures ...... 22 Materials ...... 24 3.3.1. Field materials ...... 24 3.3.2. Data treatment and software usage...... 24 3.3.3. Statistical tests ...... 26

4. Results ...... 29

General features of the data set ...... 29 4.1.1. Distance to nearest object ...... 32 4.1.2. Correlation plots ...... 34 Linear models ...... 37

5. Discussion...... 40

Discussion of results ...... 40 5.1.1. Agroecological approach ...... 42 Methodological discussion...... 44 5.2.1. Reproducibility ...... 44

5.2.2. Validity due to practical issues ...... 45 5.2.3. Data treatment and potential improvement ...... 47

6. Conclusion ...... 49

Abbreviations

CAB County Administrative Board/Länsstyrelsen

EU European Union

FID Flight Initiation Distance

GIS Geographic Information System

GPS Global Positioning System

LGB Large Grazing Birds, i.e., (in Sweden) swans, geese and cranes

NVV Naturvårdsverket/Swedish Environmental Protection Agency

SLU Sveriges Lantbruksuniversitet/Swedish University of Agricultural Sciences

T-goose /geese Target Goose/Geese. In this thesis referring to greylag goose and barnacle goose

WP Waypoint

1. Introduction

The interactions between wild geese and humans have increased in the last 50-70 years. This is true for both Europe (Fox & Madsen 2017; Fox et al. 2017) and Sweden (Montràs-Janer 2021). The way we farm affect the numbers of geese, and farming practices and productivity are reciprocally affected by the numbers and distribution of geese. There are several goose species in the world; in Sweden nine of them occur naturally and annually (Artdatabanken 2021b).This thesis targets two of them: greylag goose (Anser anser) and barnacle goose (Branta leucopsis). Hereafter collectively called target geese (T-geese). Also, if not specified differently, the words “goose” and “geese” refer to wild individuals/populations and not domesticated ones.

Together with common cranes (Grus grus) – T-geese are the bird species that render the highest number of damage reports from Swedish farmers to county administrative boards (CABs) (Montràs-Janer 2021). Between the year 2000 and 2015, barnacle geese in single species flocks or as a part of mixed flocks prompted 804 damage reports (ibid.). Greylag geese in single species flocks or as a part of mixed flocks prompted 772 damage reports (ibid.).

In contrast to many other farmland bird populations, most goose populations that are part of European flyways have benefitted from the agricultural intensification and rationalisation in the last 50-70 years (Fox & Madsen 2017). Agroecological practices, on the other hand, usually aim at diversifying the agroecosystem itself and the landscape where it’s imbedded (Wezel et al. 2014; Gliessman 2015). Agroecological practices incorporated directly on cropland include intercropping and agroforestry, but also to actively manage landscape features surrounding cropland, e.g., hedges, buffer strips and field islets. A pronounced goal of integrating or re-integrating such landscape features is to provide habitats for natural enemies to pests inside cropland (Wezel et al. 2014). Compared to an agricultural landscape of monocultures with vast fields, the mentioned agroecological practices typically lead to increased patchiness and occurrence of physical objects on and around cropland (Gliessman 2015; Snapp & Pound 2017). As geese prefer to forage on arable land with extended visibility range (Fox et al. 2017; Rosin et al 2012), it’s relevant to study how they react to scaring/disturbance

10 depending on how closely they are situated to physical objects that reduce their visibility range. Qualitative features of physical objects that reduce visibility range can be assumed to have an impact on both habitat provision for goose predators, and how much the visibility range is actually decreased. In the results of this thesis, no distinction between qualitative aspects of physical objects is however applied,Objects that are more typical of agroecological practices, e.g.,trees or other taller vegetation in buffer zones are treated the same as houses or road embankments. Of course, it would be more agroecologically relevant to only look at physical objects that are also suggested as agroecological practices. However, that would require a different study design where I would be confined by where these objects were situated and not where the geese happen to be. Data collection would take much longer time using that type of approach.

Passive or active scaring of geese from agricultural land is utilized to prevent crop damage, and one way to see how sensitive geese are to scaring, is to measure flight initiation distance (FID). It simply shows at which distance geese (or any birds) initiate flight from a human being or other actively disturbing element. Geese that show high values of FID are more easily scared away from agricultural land and can therefore be assumed to cause less crop damage. Therefore, it’s interesting to see if variables, such as distance to physical objects from geese, leads to increasing FID.

Hypothesis and aim The underlying research question for this thesis is how the dependent variable FID is affected by the independent variable of distance to surrounding physical objects. Other independent variables, that is flock constellation, flock size (number of LGB individuals) and date of the scaring trial will also be analysed, yet there’s just one main hypothesis: H1=Distance to objects taller than 70 cm above the ground, shows a significant negative association with FID among greylag and barnacle geese, i.e., ρ (rho) should be negative at an alpha level of 0.05. Corresponding null hypothesis is then: H0=There’s no negative association between distance to objects taller than 70 cm above the ground and FID for greylag and barnacle geese.

The limit is set to 70 cm above the ground, because this is roughly the height at which the eyes of T-geese are positioned when they’re standing on the ground with partly or fully stretched necks. Logically, physical objects taller than 70 have the largest relevance for visibility range among geese.

Note that mere a correlation between FID and proximity shouldn’t lead to confirmation of H1, i.e., the correlation must be negative. The statistical tests should therefore be negatively one-tailed.

In a linear model the alternative hypothesis could also be described as: ŷ = a + -b*x.

In this equation > ŷa< is the intercept of the y-axis. >-b< is the coefficient describing the negative correlation and >x< is the distance to a physical object surrounding T-geese.

The ultimate aim of the thesis is that the knowledge generated can be utilized by farmers that wish to adopt agroecological practices to combat geese as a pest on cropland.

Study design The results gathered and presented in this thesis are based on an observational prospective study design with a deductive hypothesis.

The study design is termed observational since the subjects (geese) were treated as similar as possible, and with no purpose of treating the subjects differently. Additionally, other independent variables that I measured, e.g., species constellations and distance to objects from the flocks, were out of my control. And since this is an observational study, I can just point at correlations rather than ultimate causation of FID. Although, I tried to avoid scaring the same geese more than two days in a row, I can’t be sure that this was actually the case. This is further discussed in the methodological design.

The study design is prospective because I collected the data myself and the data have not been published anywhere else.

The hypothesis formulated is deductive since it’s based on already existing hypotheses claiming that geese prefer foraging sites with good visibility compared to reduced visibility.

2. Background

Geese

2.1.1. Taxonomy and species identification In daily English and Swedish language, the words geese and gäss refer to the two genera of Anser and Branta, see figure 1.

•Aves (birds) Class

•Anseriformes Order

•Anatidae Family

•Anserinae Sub family

•Anser "[True] geese" Genera •Branta

Figure 1. Taxonomic hierarchy from birds down to true geese, i.e., the genera Anser and Branta. Sometimes the term “True geese” is reserved for the Anser and Branta genera. In this paper, no distinction is however made between geese and “True geese”.

As the species concept is debatable within biology, some goose populations are subject to taxonomic debate. In this thesis, I will however just rely on the taxonomic

13 classification by SLU Artdatabanken (2021b). This implies there are six Anser species and three number of Branta species annually and naturally occurring in Sweden, see table 1.

Table 1. Goose species annually and naturally occurring in Sweden (SLU Artdatabanken 2021b). Note that there is a taxonomic discussion about species distinction of some populations. Hybrids between species may also occur. Further subspecies could also be distinguished. T-geese in bold letters.

Anser genus Branta genus Lesser white-fronted goose - A. erytrhropus Barnacle goose - B. leucopsis Taiga bean goose - A. fabalis fabalis Canada goose - B. canadensis Tundra bean goose – A. fabalis rossicus Brent goose - B. bernicla Greylag goose - A. anser Greater white-fronted goose - A. albifrons Pink-footed goose - A. brachyrhynchus

Due to appearance similarities, some goose species can be hard to distinguish visually from each other in field: this is particularly true for bean goose, pink-footed goose and subadult greater white-fronted goose (Svensson 2009). In the case of greylag goose and barnacle goose, it’s usually much easier to distinguish them from other goose species based on plumage. Especially when they’re observed on the ground and with a field scope, as is the case in my method, see chapter 2.

Figure 2. Greylag goose – Anser anser. (Åsa Berndtsson 2004). https://commons.m.wikimedia.org/wiki/File:Gr%C3%A5g%C3%A5s_Greylag_Goose_(14341925 828).jpg

Figure 3. Barnacle goose – Branta leucopsis. (Tony Hisgett 2013). https://commons.wikimedia.org/wiki/File:Barnacle_Goose_(8677586443).jpg

2.1.2. Physiology and ecology Geese are obligate herbivores with a simple and short digestive system compared to other herbivorous species. Because of their simple digestive system, they must consume large amounts of plant tissue relatively to their body weight (Fox et al. 2017). Plant tissues with low fibre/roughage content are therefore preferred by geese. Plants with such features are commonly found inside managed agroecosystems, which may lead to intense interactions between agriculture and geese. The green revolution in the 20th century brought crops with higher proportions of protein and starch in the biomass (Fogelfors 2015; Gliessman 2015). This led to increased harvests and harvest indices (harvested biomass/residual biomass of crop) of such crops (Fogelfors 2015). These plant breeding advancements also benefitted foraging efficiency among geese as they already had adaptations to feed on crops with higher proportions of protein and starch than plants in natural ecosystems usually offer (van Eerden 2005; Fox et al. 2017; Fox & Madsen 2017). Many goose populations in Europe have therefore shifted their diet from wild plants to agricultural plants, which is especially true for wintering geese (van Eerden et al. 2005).

Multiple variables affect where geese forage, and one variable that was confirmed in a review article was the size of agricultural fields (Fox et al. 2017). The attractiveness of large fields has been shown to remain also when the same crop has been grown in fields of different size (ibid.) The plausible reason for this is that geese more easily detect and escape from predators when on large fields compared to smaller fields, thanks to the extended visibility range that larger fields provide (Vickery & Gill 1999; Rosin et al. 2012; Fox et al. 2017). The combination of large fields adjacent to roosting sites typically attract geese, and during such conditions,

15 the conflicts between farming interests and geese are accentuated (Fox et al. 2017; Nilsson et al. 2019).

Depending on life history activity, geese can be broadly categorized as

- wintering, - migrating or - breeding

T-geese belonging to each of these life history categories forage on agricultural crops in Sweden (SLU Artdatabanken, 2021b). In March, when data were collected for this thesis, geese found in Scania may be wintering, migrating, or breeding as the latter typically starts in March for European populations (Svensson 2009; Carboneras & Kirwan 2020). Diet and response to disturbance also changes depending on life history activity (Carboneras & Kirwan 2020), and that’s why it’s interesting to analyse date as an independent variable for FID. T-Geese are opportunistic foragers and a larger share of the Swedish breeding population also winter in Sweden and Northern Europe now than 15-30 years ago. (Olsson et al. 2018; Carboneras & Kirwan 2020; SLU Artdatabanken 2021b). This can be attributed to elevated winter temperatures, as well as more winter sown cereals providing food all winter (Fox et al. 2017; Olsson et al. 2018).

2.1.3. Population and damage trends As with most wild animal populations, it’s hard to know exact numbers of individuals. However – based on the criterion of International Union for Conservation of Nature (IUCN) – neither greylag goose nor barnacle goose are red listed in Sweden (SLU Artdatabanken, 2021c; SLU Artdatabanken 2021d). They are both categorized as Least Concern (LC).

There are six different monitoring systems, with somewhat different methods and objectives, that provide indices about number of T-geese in Sweden (SLU Viltskadecenter 2018). One of these monitoring systems is called Viltskadestatistik (eng. game damage statistics). Since game damage statistics focus on geese on cropland, it’s the monitoring system that has the highest relevance for this thesis. Figure 4 shows trends for damage caused by T-geese. When Montràs-Janer (2021) compared annual damage between LGB between year 2000 and 2015 in the game damage statistics, she concluded that: • Barnacle goose caused the second highest number of damage reports (804) and greylag goose caused the third highest number of damage reports (772)

• Barnacle goose caused the second highest number of yield losses (11 531 metric tonnes) and greylag goose caused the third highest yield losses (9 157 metric tonnes) • Barnacle goose caused the highest number of compensation costs to farmers (1 136 000 euros) and greylag goose caused the third highest number of compensation (738 000 euros).

Socioeconomic reasons may however also explain differences between farmers’ willingness to report damages from LGB. Moreover, one and the same farmer may also be differently prone to report damages from year to year (Montràs-Janer 2021). The drivers and factors influencing farmers’ willingness to report crop damage are also emphasized as a needed future research perspective by Montràs-Janer (2021).

Reimbursement to farmers 7000 6000 5000 4000 3000 1.000 sek 2000 1000 0 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 Year

Mixed Greylag Barnacle

Figure 4. Reimbursement to farmers due to damage from T-geese. Mixed flocks include reimbursements where T-geese were present with other LGB. The relative damage caused by T- geese in such mixed flocks is however not revealed. The graph is based on game damage statistics between 2004 and 2019. All annual reports can be found at the webpage of SLU Viltskadecenter (2021).

Reimbursement, as presented in figure 4, includes both compensation for caused damage as well as subsidies for proactive measurements. The most common proactive measures that farmers were subsidized for in 2019 included (Frank et al. 2020):

- Diversionary feeding sites to lure birds away from economically sensitive crops (mainly by spreading of grains during certain time periods).

- Different scaring measures, e.g., liquified petroleum gas canons, flags and mirror scaring devices.

- Accommodation fields where birds may graze undisturbed.

Reimbursement to farmers don’t necessarily reflect the abundance of different LGB species in Sweden, since reimbursement is paid differently depending on the species. Nonetheless, there seems to be a general correlation between crop damages and species abundance of T-geese in Sweden (Montràs-Janer 2021). Between 2000 and 2015, there was a positive correlation between population indices of T-geese and i) number of damage reports, ii) yield loss (biomass) and iii) reimbursement to farmers of T-geese. Population indices were based on autumn counts of wintering and staging T-geese in southern Sweden. The yield losses and reimbursement paid per reported damage increased for barnacle geese, but not for greylag geese between 2000 and 2015 (Montràs-Janer 2021). In other words: the number of reports on crop damage caused by greylag geese increased between 2000 and 2015.

Legislation

In response to declining bird and goose populations in the first half of the 20th century in the EU, The Birds Directive was implemented in 1979 (European Commission 2019). Together with the Habitats Directive, it provides the main framework for nature conservation and protection in the EU (ibid.). Even though the Birds Directive aims to prevent all kind of human-initiated direct disturbance and killing of wild birds within the EU, it also contains a specific article about birds causing damages directly to crops. Exemptions to disturb and kill wild birds may be given if it aims to prevent serious damage to crops, livestock, forests, fishery and water resources (The European Parliament and the Council of the European Union 2009/147). The Birds Directive also stipulates what member countries of the EU may decide nationally and what must be negotiated at multilateral level, for example what might be considered as serious crop damage.

In the Birds Directive and on Swedish national level as well, many amendments, that regulate how barnacle and greylag goose may be disturbed and hunted, have been implemented. Without making it too detailed it’s still fair to say that barnacle geese are more protected from hunting and disturbance than greylag geese are (SFS 2001:724; The European Parliament and the Council of the European Union 2009/147).

In 1995, the Swedish government initiated a system where farmers can apply to the CABs for reimbursement due to damage from animal wildlife (Montràs-Janer 2021). The legislation is not specific for geese, but also includes large predators such as wolves and bears. The rules are outlined in Viltskadeförordningen (SFS 2001:724) and Naturvårdsverkets (NVV) rules about subsidies and reimbursements

(NFS 2008:16). Reported damages are inspected and analysed by inspectors from the CAB, and thereafter reimbursement may get paid to the affected applicant/farmer depending on the culprit species.

Diversification and agroecological practices Since agroecology can be referred to as a movement, a practice and a scientific discipline, (Hazard et al. 2016; FAO 2021), it can be hard to define it. To make it more concise, Wezel et al. (2014) described 15 agroecological practices that were repeatedly found in agroecological scientific publications. To qualify as such, the practice had to contribute to at least one of the following aspects i) Efficiency increase ii) Substitution of inputs iii) Redesign of the agroecosystem or/and the surrounding landscape. Most of these practices rely on diversification of the agroecosystem and more complex food-webs compared to industrial monocultures. The redesign practices are exemplified with re-integration and incorporation of natural and semi-natural elements such as hedges and vegetation strips surrounding areas of more intense agricultural production. Agroforestry systems, where trees are more directly incorporated into areas of intense agricultural production are also identified as a redesign practice (Wezel et al. 2014). It’s therefore quite obvious that agroecological practices, aiming for redesign, would create a different farming landscape with smaller field size and more physical objects between areas of intense agricultural production and ultimately decreased visibility range for geese, which is something that geese typically avoid when they forage (Rosin et al. 2012; Fox et al. 2017).

3. Materials and methods

The data collection for this thesis was based on a method already applied in the national goose project “From field and farm to flyway” which is run by my two supervisors: Elmberg and Månsson. In addition to the method of scaring trials they had already developed, I collected data for distance to physical objects surrounding LGB flocks during scaring trials.

In total, 164 scaring trials were performed where each scaring trial can be seen as a sample. Due to data collection errors, 13 scaring trials had to be removed, thus 151 valid ones remained. Sites of scaring trials are shown in figure 8.

Figure 8. Each red flag indicates a scaring trial site, 13 of the scaring trials were later removed due to data collection errors.

Species, scaring sites and limitations Barnacle and greylag geese were targeted because they are two of the most common and main culprit species for crop damage in Sweden (Montràs-Janer 2021), see section 1.3.3. However, since T-geese often aggregate with other LGB species, the flocks that I scared could also contain other goose species and whooper swans (Cygnus cygnus) Further limitations defined in advance included:

• As specified by the hypothesis, scaring trials were only carried out on agricultural land, i.e., recreational parks, golf courses, home gardens, etc. were omitted. • Scaring trials were not performed inside nature reserves, national parks or other wildlife refuges. • My own visibility range had to be at least 500 m., this implied that scaring in darkness or foggy weather was excluded. • If something obvious disturbed the flock during the scaring trial, it was cancelled and not included in the data set. Such unintentional disturbance included other humans, predators, or vehicles.

Through the species gateway Artportalen, run by SLU Artdatabanken, I could see where T-geese had been observed by the public in the last months and in the last years (Figure 9). As I was based in Malmö, I could then confirm that there should be enough T-geese within 70 km from my home during the data collection time frame. Note, however, that the observations through Artportalen in figure 9 only mirror observations, but not standardized absences.

To at least avoid scaring the same goose individuals too close to each other in time, I didn’t visit the same area two days in a row, which was controlled with the help of my GPS. No guarantee can however be given that I didn’t scare the same goose individuals two days in a row since geese naturally cross the borders between the areas I visited. This approach is further discussed in the methodological discussion.

Figure 9 (Used under publisher’s permission). Observations by the public of T-geese between 2016.01.01 and 2021.02.06 in Scania. Darkness of squares indicate higher numbers of observations. (SLU Artdatabanken 2021a)

Scaring procedures All WPs (waypoints) were recorded with a handheld GPS. A simplification of the method is shown in figure 10. Numbers of scaring trials per day varied between one and eleven (figure 12). During all scaring trials, I wore the same plain-coloured jacket of burgundy. Colours of trousers varied between grey and green. My own height is 191 cm and my weight is 83 kg.

The data collection proceded as follows:

1. While I was driving, I looked for T-geese through my car without binoculars or other optical aides. For geographical range of scaring sites, see figure 8 above. 2. Where the traffic situation allowed safe parking the car, a scaring trial could be conducted. Free sight between myself and the majority of the individuals in the flock was a prerequisite, too. When the sight requirement couldn’t be met, I walked to a spot where there was free sight between me and the majority of the individuals in the flock. The walking direction to such a spot

was never directly towards the geese, i.e., less than 180° towards the flock.. The distance between the car and the flock varied and the only goal was to park the car so that its presence didn’t initiate flight of the flock. 3. I counted the number of individuals of each LGB species. For flocks of more than roughly 100 individuals, individuals were not counted exactly, but rather in units of five or ten. If there were species identification uncertainties, I used a field scope to get sure. 4. Waypoint (WP) 1 was registered as soon as I had exited the car or at the spot where there was free sight between the majority of geese in the flock and myself (see step 2). 5. I then walked towards the approximated centre of the flock in typical walking pace in a calm manner. Walking speed wasn’t registered but most likely varied between 3 and 6 km/h. I intended to walk as straight as possible towards the flock, but this had to be balanced against not getting too wet myself and if the crops growing in the field was likely to get seriously damaged by my footsteps. 6. When the first goose/geese in the flock, initiated flight, I stopped walking and registered WP2 with the GPS. If the flock consisted of different LGB species, it was registered in which order the species initiated flight. 7. I next walked to the spot where I assessed that the first goose/geese initiated flight and registered WP3 with the GPS. The accuracy of reaching this spot varied due to field and landscape factors, particularly muddiness and water saturation. This is a validity issue which is brought up in the methodological discussion. At WP3 I also collected data for an additional variable: - Distance to nearest physical objects surrounding WP3. With help of the compass in the GPS, objects were sought after in the four cardinal directions: i.e., north, east, south, and west. For each cardinal direction, a laser gauge was used to detect and measure distance in meters to the nearest physical object in each cardinal direction. The laser gauge was held horizontally at 70 cm above the ground at WP3. The objects had to be stationary, i.e., not moving. For instance, cars passing by were omitted. 8. After registering the observations at WP3, I walked back to the car. A new scaring/sampling could be done after a minimum of 2 km of driving, alternatively if the T-goose individuals were believed to not be the same ones that had just been scared in the previous scaring trial. This was determined by simply observing the flight direction of the previously scared flock.

Figure 10. A simplified illustration of the method. The red-marked bird shows that this is the first individual that initiated flight, and thus where WP3 was marked.

Materials

3.3.1. Field materials To accomplish the method described above, the following materials were used:

- Car (Toyota Corolla, estate car – specified since appearance and sound of the car might have an impact on the scared flocks) - Field binoculars (Nikon ProStaff 7S, 8x42) - Field scope on tripod (Lotus SP80) - Laser distance gauge (Leica RangeMaster CRF 800) - Handgeld GPS (Garmin ETrex 32X). The device utilized both GLONASS and GPS satellites. - Wooden stick of 70 cm height to control for height at WP3.

3.3.2. Data treatment and software usage

Coordinates/WPs were transferred to the BaseCamp software issued by Garmin, using the Topoactive Europe 2020.20, North East map. Coordinates were also converted from WGS84 to RT90 coordinates in ArcGis. The Pythagorean theorem, using X and Y-coordinates of the RT90 system, was then utilized to obtain the FID values.

Since distance to objects was measured in four cardinal directions, distance to object/s can be displayed in two ways:

1. Distance in meters to the nearest object in one of the cardinal directions. 2. Average distance in meters to objects, i.e., sum of distances to objects in each cardinal direction divided by the number of measurements. Since objects couldn’t always be measured in each cardinal direction, the number of measurements differed.

All data were then compiled in an Excel CSV spreadsheet which was subsequently imported into RStudio developed by the R Core Team (2020). The following packages (authors/developers in brackets) were also used for data analysis and creating the plots of this thesis. • Tidyverse (Wickham et al. 2019) • Corrplot (Wei & Simko 2017) • Lubridate (Grolemund & Wickham 2011)

In total, 164 scaring trials were performed, but 13 were removed from the analysis due to data collection errors. This rendered 151 scaring trials included in the analysis. Flock constellations could have been defined in different ways, read further in the methodological discussion, but the chosen definitions of flock constellations were:

1. Flocks containing greylag geese but no barnacle geese.

2. Flocks containing barnacle geese but no greylag geese.

3. Flocks containing both greylag and barnacle geese, i.e., mixed.

It’s the distinction above that is referred to when the simplified terms “greylag goose flocks” or “barnacle goose flocks” is mentioned in the continuation of this thesis. Keep in mind that these definitions of constellations imply that LGB species other than T-geese were sometimes also present in the flocks. Further on, this definition of flocks, doesn’t take flight initiation order between species into account.

Figure 11. Species constellations of scared T-geese flocks. Barnacle goose flocks=10, greylag goose flocks=122, mixed flocks of barnacle and greylag goose=19

Scaring trials started on 2021-02-12 and finished on 2021-03-23. The number of scaring trials was relatively evenly distributed throughout the study period (Figure 12) Note that there are different time gaps between dates when scaring trials were performed. Dates of the scaring trials were transformed to of ordinal dates of 2021 with 2021.01.01 as the start value (1) in correlation tests and linear models.

Figure 12. Number of scaring trials per day.

3.3.3. Statistical tests

By looking at figure 13, one can conclude that FID was not normally distributed. However, FID-values became normally distributed by logarithmic transformation with the natural logarithm (e≈2.72), which is shown in figure 14. Also, in all other

26 graphs and statistical tests, it’s the natural logarithm that is used when logarithmic transformation is mentioned.

Constellation-wise, Shapiro-Wilk tests showed that FID was probably (p=0.92) only normally distributed in flocks containing barnacle geese but no greylag geese. But due to the small sample size of this constellation n=10, the probability of normality shouldn’t be relied on. The overall unnormal distribution of FID among flocks, led to the decision to run the non-parametric Spearman’s correlation coefficient test, since it doesn’t require normally distributed observations.

Figure 13. Distribution of FID for all scaring trials. FID-values are clearly skewed.

Figure 14. Logarithmically transformed FID-values. Shapiro-Wilk normality test yielded a p-value of 0.432, which indicates a high probability of normal distribution in the population.

Since the alternative hypothesis required FID to correlate negatively with distance to object, the correlation tests for distance to object was decided in advance to be negatively one-tailed. Ties that appeared in Spearman’s correlation tests rendered inexact p-values, which were treated with asymptotic t-approximation.

Also when grouped by constellation, FID-values became normally distributed by logarithmic transformation in each constellation. Consequently, the linear model in figure 28 and table 3 to 5 showing multiple regression of date, flock size and distance to nearest object, is based on logarithmically transformed FID-values. Untransformed FID-values should not be considered statistically valid in the linear modelling, but for comparative purposes these are shown in appendix 3.

Besides the variables presented in the results, data for several other variables were also collected during the scaring trials, but these were omitted in results and in the linear modelling.. The entire data set, which also includes variables that weren’t analysed, is found in appendix 1.

4. Results

The results are mainly displayed through tables and diagrams with supplementary descriptions. The first section shows general aspects of the data set and FID features related to the independent variables one by one. The second section describes the results through linear modelling taking multiple independent variables into account. FID is always expressed in meters.

General features of the data set

Flight intiation order between species is shown in figure 15. Of all scaring trials, 118 flocks were single-species flocks, whereas the remaining 33 flocks were mixed, and it’s the latter 33 that are shown in figure 15. Since FID was measured as the first LGB individual/s/ initiating flight, this created multiple flight initiation orders.

Figure 15. Flight initiation order by species in flocks containing more than one LGB species. “Unknown” refers to flocks where it was not possible to determine the flight initiation order. In some cases it was not possible to tell the order of a specific species, see brackets.

Most flocks were comparitevely small in numbers of LGB individuals (see figure 16a and 16b). Only 15 out of 151 flocks contained more than 100 LGB individuals.

Figure 16a. Histogram of all flocks. Bar width is 200 individuals.

Figure 16b. Histogram of flocks containing 0-200 individuals, i.e. a blow-up of data in the leftmost bar in figure 16a.

The FID median for all flocks was 95 meters. It was higher for greylag goose flocks compared to barnacle goose flocks (figure 17), (94m and 56m respectively). The FID median for mixed flocks was 117 meters, which is closer to greylag goose flocks than barnacle (figure 17). Mean FID for all scaring trials was 105 meters. Mean FID for greylag goose flocks was 106 meters. Mean FID for barnacle goose flocks was 68 meters. Mean FID for mixed flocks was 116 meters. Non-parametric

Wilcoxon rank sum test with continuity revealed a significant difference between barnacle goose flocks and mixed flocks: p=0.0087.

Figure 17. FID (median) by species constellation. Whiskers depict the median ± 1.5* the interquartile range. Values outside whiskers are considered as outliers and depicted as dots.

Ordinal dates in correlation to FID turned out to show a negative rho of -0.2614 and to be significant (figure 18). The correlation of ordinal dates may however just be generalized to the actual data collection time frame, i.e., between 2021.02.12 and 2021.03.23. Flock size, expressed as number of individuals in the flock, returned a rho of 0.1364, but this was not significant (figure 19).

Figure 18. Scatterplot showing association between ordinal date and FID for all scaring trials. Two-tailed Spearman correlation test provided a rho of -0,2614 with p-value: 0.0012.

Figure 19. Scatterplot showing association between the number of goose individuals in a flock and FID for all scaring trials. Two-tailed Spearman correlation test provided a rho of 0.1364 with p- value: 0.0948.

4.1.1. Distance to nearest object In direct contrast to the hypothesis, there was a slightly positive correlation between distance to nearest object and FID. When all scaring trials were included, rho was estimated at 0.1588 with a p-value of: 0.97. Also, when looking at greylag goose flocks and mixed flocks separately, rho was positive. But even constellation-wise, very high p-values were obtained, and these are shown in the captions to figure 20- 23. The high p-values can be attributed to the correlation-tests being negatively one- tailed when looking at distance to nearest object. If the correlation-tests instead would have been two-tailed, the p-values would have been much lower. The motivation behind running negative one-tailed tests can be attributed to the hypothesis assuming a negative correlation.

Figure 20. Scatterplot showing distance to nearest object and FID for all scaring trials. One- tailed negative Spearman correlation test provided a rho of 0.1588 and a p-value of 0.97.

Figure 21. Flocks containing greylag geese but no barnacle geese. Other LGB species may be present in the flock. One-tailed negative Spearman test provided a Rho of 0.1722 and a p-value of 0.97.

Figure 22. Flocks containing barnacle geese but no greylag geese. Other LGB species may also be present in the flock. One-tailed negative Spearman test provided a Rho of -0.0502 and a p-value of 0.45. Note the small sample size, n=9, for observations that contained pairwise observations of both variables.

Figure 23. Flocks containing both greylag geese and barnacle geese, i.e., mixed. Other LGB species may also be present in the flock. One-tailed negative Spearman test provided a Rho of 0.4611 and a p-value of 0.958.

4.1.2. Correlation plots The correlation matrix plots shown in figure 24 to 27 offer the possibility to compare several variables simultaneously, and it’s also a way to detect multicollinearity between independent variables. In the correlation matrix plots, it’s shown that Average distance to nearest object shows comparatively high correlation with Distance to nearest object. This is not surprising since Average distance to object takes objects in all cardinal directions into account and that one of them per se is Distance to nearest object, see section 2.3.2.

Size of the circles in figure 24 to 27, indicates, just as colour, the strength of the correlation.

Figure 24. Correlation between variables for all scaring trials. The black background is just to distinguish it from the constellation specific correlation plots below. The matrix is based on two- tailed Spearman correlation coefficient tests.

Figure 25. Correlation between variables in greylag goose flocks. The matrix is based on two- tailed Spearman correlation coefficient tests.

Figure 26. Correlation between variables in barnacle goose flocks. Keep the small sample size in mind, n=9. The matrix is based on two-tailed Spearman correlation coefficient tests.

Figure 27. Correlation between variables in mixed flocks. The matrix is based on two-tailed Spearman correlation coefficient tests.

Linear models

Based on figures 20 to 23, the hypothesis that FID would show a negative correlation to nearest object, can be refuted. Even though the correlations for greylag goose and mixed flocks were positive, it’s still interesting to inquire the coefficient distance to nearest object in a multiple regression with other variables, namely: ordinal date; flock size and flock constellation. P-values in the multiple regression are based on two-sided testing in contrast to the one-sided testing related to figure 20 to 23.

Stars next to p-values indicate levels of significance: <0.001=***, <0.01=**, <0,05=*. The coefficients that turned out to be significant were the intercepts for greylag goose flocks (table 3) and mixed T-goose (table 5) flocks and finally Distance to nearest object in greylag goose flocks (table 3).

Table 3. Coefficients of the multiple linear regression describing greylag goose flocks. FID-values have been logarithmically transformed in order to obtain normally distributed FID-values, see section 3.3.3.

Coefficient Estimate Std. error P-value Intercept 4.97 0.365 2e-16*** Distance to nearest 0.002 0.0008 0.012* object Ordinal date -0.008 0.004 0.08 Flock size (number 0.004 0.002 0.135 of individuals)

Table 4. Coefficients of the multiple linear regression describing barnacle goose flocks. FID- values have been logarithmically transformed in order to obtain normally distributed FID-values, see section 3.3.3. Coefficient Estimate Std. error P-value Intercept 4.006 2.348 0.149 Distance to nearest -0.0007 0.003 0.831 object Ordinal date 0.0002 0.286 0.996 Flock size (number 0.0004 0.0004 0.446 of individuals)

Table 5. Coefficients of the multiple linear regression describing mixed flocks. FID-values have been logarithmically transformed in order to obtain normally distributed FID-values, see section 3.3.3. Coefficient Estimate Std. error P-value Intercept 4.536 0.664 2.82e-05*** Distance to nearest 0.003 0.001 0.052 object Ordinal date 0.0001 0.009 0.991 Flock size (number -0.0001 0.0003 0.74 of individuals)

The relative importance of Distance to nearest object can also be shown in linear multiple regression curves where all other variables are kept equal (figure 28), in this case at mean values for each variable in each constellation. Note that all variables from the tables above are included, also those that didn’t show any significance. As expected from the coefficients in table four, barnacle goose flocks show a negative correlation with FID, but this result should not be relied on since the p-value was >0.05.

Figure 28. Linear model for all constellations using logarithmically transformed FID-values and the range of values for Distance to nearest object. Mean values for variables other than “Distance to nearest object” are used. The regression lines are expressed below.

Barnacle: y(log(FID))=-0.0007036*Distance to nearest object + 0.0003696*Mean number of individuals + -0.0001520*Mean ordinal date + 4.0062753 (Intercept)

Greylag: y(log(FID))=0.0020880*Distance to nearest object*+ 0.0040289*Mean number of individuals + -0.0055520*Mean ordinal date+ 4.9709891 (Intercept)

Mixed: y(log(FID))= 0.0033715*Distance to nearest object + -0,0001154 *Mean number of individuals + 0.0001126*Mean ordinal date + 4.5359580 (Intercept)

5. Discussion

Since the hypothesis was clearly refuted, a fairly large part of the discussion is devoted to reproducibility and validity issues.

Discussion of results

The hypothesis was that proximity to physical objects would show a negative association with FID-values among T-geese. On the contrary, there was a positive correlation between distance to nearest object and FID for greylag goose flocks and mixed flocks. Among barnacle geese, there was still a negative correlation between distance to nearest object and FID. But the small sample size of barnacle goose flocks, couldn’t bring any significance to such correlation, see figure 22. In the multiple linear regression model of logarithmically transformed FID-values (figure 28), where several variables were considered, the relative effect of Distance to nearest object on FID decreased. But as none of the other variables in the multiple regression were significant, no conclusion of the interplay between Distance to nearest object and other variables can be made.

Field size and visibility range correlate positively with forage selection among geese in several studies. The general trend is that geese prefer agricultural fields of larger size over smaller fields (Rosin et al. 2012; Fox et al. 2017). The guidelines from NVV and SLU Viltskadecenter, bring up that incorporation of physical objects such as cover hedges and unharvested stalks of crops can be utilized as a strategy to avoid damage on cropland from LGB (Månsson et al. 2015). The results from this thesis don’t support that geese are more easily scared in such habitats. But even though the results showed that scaring isn’t facilitated by proximity to physical objects, it could still be the case that T-geese cause less damage in fields with such features as they may still prefer to land on fields far from these kind of physical objects. The method applied in this thesis has only inquired where geese were already present, i.e., not inquired which fields that were avoided by T-geese. No efforts were made to inquire whether T-geese significantly avoid fields as a function of distance to physical objects from the very beginning.

Even though it doesn’t answer the hypothesis, it was interesting to see that barnacle goose flocks showed a significantly lower FID than mixed flocks (figure 17). This is logical if one considers that barnacle geese are far more protected from hunting than greylag geese in the EU and Sweden (The European Parliament and the Council of the European Union 2009/147). It’s therefore anticipated that barnacle show less sensitivity to humans than do greylag geese. Even though the difference between barnacle goose flocks and mixed flocks turned out to be significant, one should keep the small sample size of barnacle goose flocks in mind (n=10). If it holds true that mixed flocks of T-geese and other LGB are more easily scared compared to single species flocks, this can have obvious scaring management implications. If farmers start preferring mixed flocks rather than single species flocks on their fields, due to the facilitated scaring potential, it could also mean that the task for inspectors working at CAB becomes harder. This is since reimbursement from CAB is paid differently by species and a part of the CAB inspector’s job is to determine which species that has caused the damage (Månsson et al. 2018; SFS 2001:724). Montràs-Janer (2021) also points out that it’s desirable that culprit species are always specified in the damage reports. Then it’d be easier to predict in which areas that conflicts between farming interests and T-geese may arise (ibid.). However, FID may also vary between regions, for example did similar scaring trials around Kristianstad show that barnacle goose flocks didn’t turn out to exhibit significantly lower FID-values than other constellations (appendix 2).

A free reflection during scaring trials of barnacle goose flocks, was that these flocks were more restless than greylag flocks/individuals. This wasn’t measured, but during scaring trials of barnacle goose flocks, the scared individuals often just started to circulate around me after flight initiation. This contrasts with greylag geese which I found much more determined in their flight direction after flight initiation. No general conclusion can however be based on this unsystematic observation. But given the problem of moving around geese between crop fields causing conflicts between farmers (Månsson et al. 2015; Månsson et al. 2018), it stresses the needs to understand what they do and where they head after being scared. Related to my own research questions, it would be interesting to see how far T-geese fly after scaring as a function of distance to physical objects and patchiness of the landscape.

Habituation to scaring devices among LGB, is one of the main issues in scaring strategies and management of such populations (Månsson et al. 2015; Fox et al. 2017). Habituation can be described as an animal learning to not respond to a stimulus (Raven et al. 2011). Even if the animal initially has genetically adapted instincts to respond to a specific stimulus, the response may decrease if the stimulus repeatedly doesn’t affect fitness positively or negatively (Raven et al. 2011). Thus, when stimuli, e.g., scarecrows, or physical objects such as trees or topography, are

41 exposed to a goose, the initial response of the goose might be strong, but as they get repeatedly exposed to it, the weaker the response becomes, which is the process of habituation. This could be elaborated much further, but it emphasizes the need that visual stimuli such as physical objects, eventually must imply fitness- reducing/lethal attacks if geese are not to habituate to physical objects. A potential explanation to why the hypothesis was refuted might be that T-geese in the study area have encountered too few attacks from predators such as foxes that depend on physical objects and patchiness of the landscape. Thereby visibility range isn’t as important as it is for other goose populations that have been more frequently exposed to physical objects as an agent of natural selection.

5.1.1. Agroecological approach So far, the discussion might seem a bit reluctant to acknowledge that proximity to objects couldn’t be associated with higher FID-values. This might stem from agroecology’s encouraging view of patchiness and more physical objects in the agricultural landscape (Thies & Tscharntke 1999; Wezel et al. 2014). The belief in agroecology is that such landscape features increase the interactions and food webs between biotopes of intense agricultural productivity, biotopes of reduced human disturbance, and natural ecosystems. Eventually such an approach is believed to contribute to ecological intensification that depends less on external inputs and fossil fuels (Tittonell 2014; Wezel et al. 2014; Gliessman 2015).

Gliessman (2015) suggests five steps of conversion to agroecological food systems. And as the name “food system” implies, this includes looking beyond agroecosystems themselves, and to also consider social and economic issues. The first three levels of agroecological conversion consider sustainability within agroecosystems (Gliessman 2015). The hypothesis of this thesis is typically an issue that’s regarded within conversion level one to three. Level four, on the other hand, moves beyond farm level, as its goal is to: “Re-establish a more direct connection between those who grow the food and those who consume it.” Gliessman (2015, p. 348). Although the data collected for this thesis didn’t aim to reveal any possibilities of reaching level four, it’s still interesting to consider goose management on level four. One way to do so, is to look at how the public (consumers in Gliessman’s definition of level four) view geese. This has been done by Eriksson et al. (2020). By random sampling, a survey was e-mailed to adult citizens living in the goose rich municipalities of Örebro and Kristianstad. The aim was to reveal attitudes towards geese and to find predictors of acceptance towards geese. One of the results was that 71 % of the respondents in Kristianstad and 60 % of the respondents in Örebro were positive to have geese in Sweden (Eriksson et al. 2020). However, 36 % of the respondents in Kristianstad and 48 % of the respondents in Örebro thought that the numbers of geese were too high in their home municipality (ibid.). Eriksson

42 et al. (2020) wonder if this may be a result of geese being part of the culinary traditions of Scania, and that people are more used to geese in Kristianstad than in Örebro. In areas where geese are more profoundly considered a local pest and a problem to the public on beaches, parks, golf courses etc., farmers could connect with consumers by telling them how they work to hamper damage and disturbance to humans from geese on a landscape level. But as Gliessman points out, this requires short links between producers and consumers (2015). In areas where geese are considered more problematic by the public, consumers should be more willing to pay attention to farming practices that aim to reduce goose populations on a regional scale. As Eriksson et al. (2020) show, acceptance towards geese differ depending on geographical area. Hence, the communication and connection between farmers and consumers could be differently successful depending on the region and the magnitude of how the pubic perceive geese as a nuisance.

Finally – even if an agroecological approach of patchiness and ecological intensification of agroecosystems, would be successful to combat geese as a pest, there would still be challenges. Notably because the general prediction by the European Environment Agency (EEA) (2019) is that agricultural productivity in southern Europe will be worse affected by climate change than in northern Europe including Sweden. EEA believes that this might increase the relative importance of northern European countries, e.g., Sweden, for the food supply of entire Europe. In that perspective, there will likely be stronger incentives to use more external inputs and relying less on agricultural diversification to combat geese as a pest in Sweden. Wezel et al. (2014) also disclose a pattern of agroecological practices being less disseminated in naturally fertile agricultural areas with high productivity. If Sweden’s relative importance for Europe’s food security increases, it could lead to short term productivity intensification of Swedish agriculture. In such case, agroecological practices are less likely to be favoured in Sweden. Instead, it’s more likely that more direct methods such as direct killing of geese and scaring measures based on intensive supply of external inputs, e.g., liquified petroleum gas canons or drones, are favoured. Such rather direct measures would not demand the redesign of conventional agroecosystems that agroecological practices imply. Direct killing or scaring through external inputs may not be environmentally harmful in themselves, but with agroecological practices many other sustainability benefits are enhanced too. Not at least since habitat destruction has been the main driver of biodiversity loss in the last century (IPBES, 2019). For farmland birds, the trends have been particularly adverse since 1980 (Pan-European Common Bird Monitoring Scheme, 2021), see figure 29. Negative side effects on farmland bird species/populations from increased hunting or use of external inputs such as liquified petroleum gas canons to regulate goose populations, must be carefully

43 evaluated. Hopefully, the Birds Directive should probably serve to not let such side effects become too adverse on farmland birds other than geese.

Figure 29 (Used under publisher’s permission). Species abundance indicator for birds grouped by habitat. (Pan-European Common Bird Monitoring Scheme 2021).

Methodological discussion

5.2.1. Reproducibility Since the method used for this thesis utilized an observational design with uncontrolled variables, it’s not possible to repeat the sampling procedure and obtain the same results. Mainly because it cannot be determined exactly where in the landscape T-geese will be present, i.e., which agricultural/crop fields they would be in and how closely they would be situated to physical objects.

However, some methodological facets could easily be repeated in other scaring trials:

- The area where scaring trials took place. Even though it’s not possible to use exactly the same scaring sites, one could set up rules to carry out scaring trials in a certain vicinity of scaring trial sites. All coordinates of scaring sites are found in appendix 4.

- The seasonal and diurnal time frame when scaring trials were performed.

- The appearance and walking pace of the person scaring the flocks.

- To only conduct scaring trials when the visibility range is minimum 500 meters for the human naked eye.

- To only conduct scaring trials on agricultural land.

5.2.2. Validity due to practical issues Practical validity issues of obtaining correct FID-values concern whether actual methods and materials could measure FID and distance to object in a precise way. During the data collection phase, I thought about many different validity issues. Below are the ones that I personally found most striking:

• The distance between the car/WP1 and the flock differed quite a lot. The longer distance from the flock I had to park the car, the longer time it usually required to count the geese. The time I was visible to individuals in the flock before I approached the flock might have had an impact on FID. But this was not measured. • Due to muddiness and water saturation in some fields it was hard to walk straight between WP1 and WP3 (figure 30). The longer detour I had to make to reach WP3, the less precise could I be in reaching the de facto spot of flight initiation. Fields that were easier to walk on should therefore show better precision for reaching WP3 and eventually FID. • Additionally: In fields that were extremely muddy (figure 30), walking pace towards the geese was slowed down. Walking speed per scaring trial was not measured. • At WP3 the slope of the ground varied. This made it sometimes hard to horizontally stabilize the 70 cm stick that the laser gauge was put on. Scaring trials at sites with less slope, probably show better precision for measuring distance to objects. • When the conditions were wet and muddy at WP3, the 70 cm stick tended to slightly penetrate the ground during measurement of distance to objects. This probably yielded shorter distances to objects as the measuring height to objects was lower than 70 cm. Ultimately measurements in wet and muddy fields yielded lower values of distance to objects than was the case. • I couldn’t be sure how many times I scared the same goose individuals. Although I had a method for avoiding scaring the same individuals the same day or two days in a row (see section 2.1 and 2.2), this might still have occurred. A result of this could be habituation among the scared T-

geese towards the scaring trials themselves. A way to test for possible habituation and the risk of scaring the same goose individuals the same day, could have been to test if there were significant differences in FID depending on the time of the day. No such tests were however run. The comparatively large geographical range of scaring sites (figure 8) should at least serve to diminish the risk of scaring the same goose individuals too frequently.

Figure 30. This is what the boots looked like just after some of the scaring trials. The muddiness of the field and the weight of the boots had an obvious impact on walking speed towards the flocks.

To summarize, some validity issues regard the accuracy of FID-values, whereas some regard accuracy of distance to physical objects from WP3. Better precision of FID-values could be obtained by harnessing goose individuals equipped with GPS- collars. Better precision of measuring distance to objects from WP3 and general landscape features could be solved with GIS or other mapping techniques. Of course, it would also have been possible to measure distance to objects in other directions than the four cardinal directions. But to measure which was actually the closest object in 360° around WP3 would have been very time-consuming given the materials at hand. The number of scaring trials performed, i.e., sample size, would have suffered dramatically.

Finally, it’s worth to reflect on whether FID is a good indicator of how easily T- geese are to scare and prevent from causing damage on crops. Many other options are theoretically possible to measure how T-geese react to scaring or disturbance. For instance, how they fly away after a conducted scaring trial, or how and in how

46 big groups they regroup after a scaring trial. If a scared flock is split into many smaller subgroups, the damage on crops is probably smaller and not as locally adverse. Not surprisingly, farmers are more inclined to report crop damage to CABs when they encounter larger rather than smaller flocks on their cropland. (Montras- Janer 2021).

5.2.3. Data treatment and potential improvement

Division and definition of flock constellations could have been done differently. Flocks constellations could also have been divided by:

- The first species that initiated flight could have defined the constellation. But this would also imply that LGB species other than greylag or barnacle would constitute flock constellations (figure 15). - Flocks could have been defined by the numerically dominant species in the flock.

Even though FID in my case only caught the first flight initiating individual/s, it doesn’t reveal the flight initiation distance for the subsequent goose/LGB individuals in the flock. It might be that the first flight initiating individuals/s, initiated flight much earlier than the subsequent individual/s. but the data collected here, doesn’t reveal such gradual FID of individual/s within the flock. Such an approach would require spending far more time on each scaring trial. Probably, it’d also require to be more than one person in field: One who performs the actual scaring trials/walking, and another person who counts the species and their numbers of individuals for the gradually yielded FID-values. Large flock size should imply that it’s more likely to encounter some individual/s in the flock that exhibit high FID-values. Simply because there is higher potential of variation of individual FID- values in a flock with many individuals compared to flocks of fewer individuals. . There was a positive correlation between flock size and FID, but it was not significant (figure 19). Perhaps this shows that geese are more confident and not as easily disturbed when they occur in larger groups, and that’s why there’s not a stronger correlation and effect of flock size on FID.

Finally, many more statistical tests and graphs could have been run and shown. For instance, the correlation tests were only run with the untransformed FID-values, whereas the linear modelling, uses the logarithmically transformed values. The correlation tests, could of course too, had been run and shown with the logarithmically transformed FID-values, too. The data presented in the results don’t pay any respect to qualitative features (e.g., hedge or tree or car embankment) of the physical objects. But actually, this was registered during the data collection, too.

Parametric Anova or Ancova could then have been applied on the logarithmically transformed FID-values to see if there were any qualitative differences between objects. For instance, to see how physical objects of agroecological characteristics, e.g., perennials differed from other physical objects, e.g., road embankments The data set with qualitative descriptions of the physical objects is however included in appendix 1. I do happily share information about how the categorization of objects was done.

6. Conclusion

Geese are known to prefer large agricultural fields and search for fields that provide good visibility. Based on these adaptations in geese, this thesis set out to test if distance to physical objects had an influence on how easily geese are scared on agricultural land.

In February and March 2021, 151 scaring trials were performed on flocks of greylag and barnacle geese in Scania. The hypothesis was that barnacle and greylag geese would be more sensitive to scaring/disturbance if they were closer to physical objects such as trees, road embankments and topographical slope. The hypothesis was clearly refuted. Flocks that contained greylag geese but no barnacle geese, and mixed flocks of barnacle and greylag geese were even less sensitive to scaring/disturbance when they were closer positioned to physical objects. Flocks containing barnacle geese, but no greylag geese, were more easily scared when they were closer to physical objects, but this wasn’t significant, and no inference can be drawn to the population.

Validity issues during data collection were present as the accuracy of measuring FID and distance to objects could sometimes be questioned. For instance, it was harder to measure these variables when it was wet and muddy. A more accurate way to measure distance to objects would probably be with GIS. All coordinates of the scaring trials are found in appendix 4 for possible GIS analysis.

Since agroecology aims to balance different pest problems against each other, the idea of a patchier agricultural landscape should still not be dismissed in a holistic analysis of different pest problems. As such, these landscapes may provide many other ecosystem services and can boost ecological intensification in agriculture. But given how barnacle geese and greylag geese behave in Scania during the data collection time frame, there’s no evidence to say that a patchy landscape with more physical objects facilitate scaring of geese.

Potential future research questions from an agroecological approach towards the issue of geese as a pest in agriculture include:

• Do geese, that are part of different food webs, exhibit different sensitivity to distance to physical objects? I.e., How important are predator attacks on suspiciousness/sensitivity to physical objects and patchiness of agricultural landscapes among geese?

• In regions where geese are perceived as a nuisance in parks, golf courses etc. by the public: What’s the potential of farmers communicating to the public/consumers how they work to decrease local goose pressure. Can such communication contribute to level four of agroecological conversion? I.e., can such communication contribute to connect those who produce the food and those who consume it?

References

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Eriksson, L., Johansson, M., Månsson, J., Redpath, S., Sandström, C. & Elmberg, J. (2020). The public and geese: a conflict on the rise? Human dimensions of wildlife, vol. 25 (5), pp. 421–437 ABINGDON: Routledge.

European Commission (2019). The Birds Directive: 40 years of conserving our shared natural heritage (doi: 10.2779/87814). https://op.europa.eu/en/publication-detail/-/publication/93b64009-7c3b- 11e9-9f05-01aa75ed71a1/language-en/format-PDF/source-192343919

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Appendix 1

Below is the entire dataset except coordinates that instead are found in appendix 4. Remember that just a few of the variables were finally used in the analysis. Please zoom in to fullest extent to read the observations in the table.

FID Avstånd Number Number accordin mellan Scaring of og Number g to laser WP 1 och Weather/ measWit whooper Number Number Number Number Number Number greater Number of bean Total Flight flight walking gauge 3 enl. M to Name precipitat hout swans of mute of of of tundra of taiga of white- of pink- unspecifi number initiation wind wind direction direction Crop Crop Height of avstånds avstånds object N Object M to Object M to Class of M to Class of WP in BaseCamp Date Time Route Site (ELIAS) ion reache whooper swans greylag barnacle bean bean canada fronted footed ed of LGB order FID speed direction bearing bearing type stage crops mätare mätare N class N objecto E class E object S object S object W object W 1-3 2021-02-12 10:41:00 Southern Vellinge sö Elias Sunny walking 60 8 68 1. greylag 2103.85172 3.6 N 250 204 winter whegrowing 0-15 NA NA 4-6 2021-02-12 12:06:00 Southern ReningsverElias Sunny walking 36 36 NA 62.738419 3 N 161 230 without reagrowing 16-30 45 NA 7-9 2021-02-12 12:30:00 Southern KlagshamnElias Sunny walking 4 4 NA 131.56987 4.1 N 350 356 winter whegrowing 0-15 127 NA 13-15 2021-02-12 14:04:00 Eastern Södra SandElias Sunny walking 125 10 15 150 1. greylag 266.517529 3.2 NV 310 253 winter whegrowing 0-15 63 NA 16-18 2021-02-12 14:32:00 Eastern Södra SandElias Sunny walking 10 10 550 570 1. greater w56.692528 4 NV 150 135 ley growing 0-15 54 NA 25-27 2021-02-18 10:38:00 Northern Alnarp Elias Cloudy walking 14 14 NA 59.555389 5.5 SO 81 52 winter ceregrowing 0-15 68 88 28-30 2021-02-18 11:34:00 Northern Norra LomElias Cloudy walking 5 200 205 1. greylag 2180.14627 4 O 174 190 winter ceregrowing 0-15 85 207 55-57 2021-02-22 12:22:00 Southern KlagshamnElias Sunny walking 2 2 NA 146.24837 3.5 S 160 47 winter whegrowing 0-15 140 153 520 house 733 house 285 Without re 428 tree 58-60 2021-02-22 12:55:00 Southern ReningsverElias Sunny walking 7 115 122 1. barnacle68.150729 2.6 SO 160 265 winter ceregrowing 0-15 63 105 76 tree 116 previous ye 25 tree NA NA 61-63 2021-02-22 13:07:00 Southern ReningsverElias Sunny walking 11 11 NA 80.217377 2.8 S 63 240 without reagrowing 16-30 66 NA 114 vass 130 tree Without reWithout re 173 vass 64-66 2021-02-22 13:39:00 Southern Tygelsjö Elias Sunny walking 5 5 NA 62.134383 2.8 S 160 150 winter whegrowing 0-15 77 132 146 NA Without reWithout reWithout reWithout reWithout reWithout re 67-69 2021-02-22 14:59:00 Southern Höllviken Elias Sunny walking 17 17 NA 86.476186 2.5 SO 50 & 283 314 ley growing 0-15 80 152 14 last year's 48 last year's Without reWithout re 230 car emban 70-72 2021-02-22 15:26:00 Southern Rängs sandElias Sunny walking 15 15 NA 172.08383 2.5 SO 120 283 winter ceregrowing 0-15 155 243 609 house Without reWithout re 253 house house 360 73-75 2021-02-22 15:51:00 Southern Rängs sandElias Sunny walking 72 72 NA 210.28692 2.8 SO 121 119 winter ceregrowing 0-15 NA 235 Without reWithout re 72 last year's 34 tree Without reWithout re 85-87 2021-02-23 10:29:00 Free VismarslövElias Cloudy walking 75 75 1. greylag 291.095437 3.2 SV 145 147 winter ceregrowing 0-15 85 170 25 topography 15 topography 111 topography 79 topography 88-90 2021-02-23 11:05:00 Free Genarp Elias Cloudy walking 8 8 NA 116.24958 2 SV 327 340 winter ceregrowing 0-15 110 127 253 tree 120 house 120 car emban 111 topography 91-93 2021-02-23 11:22:00 Free Genarp Elias Cloudy walking 27 4 31 unknown 108.94186 0 NA 340 9 winter ceregrowing 0-15 100 166 450 tree 60 topography 265 car emban 33 tree 94-96 2021-02-23 12:05:00 Free Utkanten aElias Cloudy walking 20 20 NA 173.35298 2.7 V multiple 24 winter ceregrowing 0-15 190 NA 5 topography 16 topography 92 topography 101 topography 97-99 2021-02-23 14:33:00 Free Svarte Elias Cloudy walking 12 12 NA 55.712234 1.4 S 265 13 winter rapegrowing 0-15 43 148 532 topography 31 topography 85 topography 312 tree 100-102 2021-02-23 14:55:00 Free Svarte Elias Cloudy walking 4 4 NA 62.051814 2.5 SV 173 211 winter ceregrowing 0-15 53 128 92 topography 74 topography 18 topography 46 topography 103-105 2021-02-23 15:38:00 Free Skivarp Elias Cloudy walking 6 6 NA 127.17028 1.2 SO 157 146 winter ceregrowing 0-15 125 NA 90 tree 28 topography 38 topography 65 car emban 106-108 2021-02-23 16:15:00 Free Önnarp Elias Cloudy walking 5 5 NA 117.61317 1 S 156 162 winter ceregrowing 0-15 121 245 96 topography 97 topography 200 topography 74 bushes 109-111 2021-02-23 16:45:00 Free Önnarp Elias Cloudy walking 15 15 NA 100.57450 0 NA multiple 109 sugar beetsharvested 0 NA 177 109 tree 179 topography 30 topography 12 topography 112-114 2021-02-24 10:23:00 Northern Bjärred Elias Cloudy walking 5 700 705 unknown 108.86060 4.2 S multiple 150 without reagrowing 0-15 112 264 131 topography 142 reed aggre 172 topography 77 topography 115-117 2021-02-24 11:14:00 Northern Borgeby Elias Cloudy walking 15 15 NA 142.62507 4.5 S 262* 230 sugar beetsharvested 0 134 312 116 topographyNA topography 48 topography 400 topography 118-120 2021-02-24 13:17:00 Northern Barsebäck Elias Cloudy walking 51 10 61 NA 176.06015 4.8 SV 97 24 winter whegrowing 0-15 125 180 195 car emban 242 car emban 196 car emban 378 house 121-123 2021-02-24 13:40:00 Northern Barsebäck Elias Cloudy walking 50 50 NA 82.646782 3 S 66 142 winter ceregrowing 0-15 NA 82 68 car emban 47 reed aggre 20 topography 54 topography 124-126 2021-02-24 14:48:00 Northern Löddeköpi Elias Cloudy walking 35 550 585 1. greylag 2140.07894 4 SV 282 18 potatoes harvested 0 124 185 109 topography 185 topography 170 topographyWithout reWithout re 127-129 2021-02-24 15:51:00 Northern Borgeby Elias Cloudy walking 7 7 NA 56.444153 4 SV 150 & 260 14 winter ceregrowing 0-15 55 86 67 topography 92 topography 57 topography 47 car emban 130-132 2021-02-24 16:25:00 Northern Borgeby Elias Cloudy walking 500 500 NA 64.146518 2.8 S Multiple 360 without reagrowing 0-15 NA 335 66 tree 186 reed aggre 90 reed aggre 73 reed aggre 133-135 2021-02-27 10:01:00 Eastern Getinge Elias Sunny walking 70 70 NA 173.76746 7.5 V 315 176 ley growing 0-15 NA 263 102 topography 187 tree 148 NA 75 car emban 136-138 2021-02-27 10:50:00 Eastern GårdstångaElias Sunny walking 15 15 NA 159.64447 5.5 V 5 118 Klöverley growing 0-15 159 237 197 tree 259 tree 269 vindkraftve 512 tree 139-141 2021-02-27 12:03:00 Eastern GårdstångaElias Sunny walking 7 6 13 1. greylag 2138.82930 6 V 19 276 winter ceregrowing 0-15 109 237 67 topography 83 topography 243 car emban 330 halmbal 142-144 2021-02-27 12:50:00 Eastern GårdstångaElias Sunny walking 60 60 NA 46.837087 5 V Fairly diver 344 Probably ugrowing 15-30 55 NA 55 tree 42 car emban 73 house 146 tree 145-147 2021-02-27 15:12:00 Eastern Harlösa Elias Sunny walking 1880 1880 NA 116.68872 5.8 V Multiple 226 winter ceregrowing 0-15 90 334 214 tree 180 topography 178 tree 213 tree 148-150 2021-02-27 15:39:00 Eastern Revingeby Elias Sunny walking 15 2 17 unknown 183.24252 4.5 V 243 180 Clover ley wgrowing 0-15 NA 247 243 topography 126 topography 179 tree 153 topography 151-153 2021-02-27 16:05:00 Eastern Revingeby Elias Sunny walking 6 6 NA 161.99140 4.3 V 246 190 winter ceregrowing 0-15 151 174 36 dike med fj 209 house 314 tree 14 dike med p 154-156 2021-03-02 09:24:00 Southern KlagshamnElias Cloudy walking 8 35 43 1. barnacle147.14557 2 NV 246 vitk & 96 winter ceregrowing 0-15 118 500 219 tree Without reWithout re 149 tree 524 house 157-159 2021-03-02 10:02:00 Southern KlagshamnElias Cloudy walking 2 2 NA 54.038468 2 NV 178 195 winter ceregrowing 0-15 43 116 Without reWithout re 238 tree 795 house 20 reed aggre 162-164 2021-03-02 10:43:00 Southern Skumparp Elias Cloudy walking 3 3 NA 108.28250 2.7 NV 108 46 winter ceregrowing 0-15 95 223 83 avfall 434 tree 178 car emban 148 car emban 165-167 2021-03-02 11:24:00 Southern Gessie Elias Cloudy walking 2 7 9 1. greylag 2101.48852 4 NV greylag 308 281 raps growing 0-15 90 199 51 Röningsjvir 132 topography 25 topography 34 topography 168-170 2021-03-02 12:51:00 Southern Västra Gre Elias Cloudy walking 12 12 NA 200.11524 3.4 NV 222 226 winter ceregrowing 0-15 180 263 41 topography 62 dike med re 206 topography 46 topography 171-173 2021-03-02 14:55:00 Southern Höllviken Elias Cloudy walking 4 4 NA 65.875102 3.2 NV 320 333 ley growing 0-15 63 181 50 reed aggre 73 dike med re 480 house 506 tree 174-176 2021-03-02 15:56:00 Southern Rängs SandElias Cloudy walking 31 148 179 unknown 86.359816 2.2 V Multiple. b 131 winter ceregrowing 0-15 78 399 158 jordhög 38 topography 370 topography 783 tree 177-179 2021-03-02 16:37:00 Southern Skegrie Elias Cloudy walking 43 8 51 unknown 242.45253 3.1 V 325 314 winter rapegrowing 0-15 220 355 580 topography 281 topography 193 car emban 201 topography 180-182 2021-03-04 11:57:00 Free FjällfotasjöElias Sunny walking 29 5 34 1. greylag 2117.25646 2 N multiple 289 winter whegrowing 0-15 119 NA 25 topogradi 29 topography 53 topography 29 topography 183-185 2021-03-04 12:29:00 Free Hönsingeh Elias Sunny walking 11 11 NA 357.58023 NA NA NA 34 winter whegrowing 0-15 NA 429 NA NA NA NA 283 topography 144 topography 186-188 2021-03-04 15:29:00 Free Steglarp Elias Cloudy walking 11 11 NA 93.544718 1.6 Ö 245 132 winter whegrowing 0-15 69 144 103 car emban 107 previous ye 91 previous ye 92 topography 189-191 2021-03-04 15:57:00 Free Västra VemElias Cloudy walking 32 32 NA 77.946006 2.1 V 13 167 winter whegrowing 0-15 NA 141 20 topography 13 topography 114 topography 82 topography 192-194 2021-03-04 16:12:00 Free Lindby Elias Cloudy walking 3 3 NA 69.975529 2.2 S NA 216 winter whegrowing 0-15 NA 304 126 topography 290 topography 232 topography 32 topography 195-197 2021-03-04 16:40:00 Free Önnarp Elias Cloudy walking 31 31 NA 91.111635 0 NA Multiple. b 111 sugar beetsharvested 0 82 118 13 topography 67 topography 68 topography 64 topography 198-200 2021-03-04 17:06:00 Free Grönby Elias Cloudy walking 50 7 57 1 barnacle 132.51743 1.7 S Multiple 130 Clover ley wgrowing 0-15 NA NA 26 topography 387 topography 80 topography 42 topography 201-203 2021-03-05 11:04:00 Northern Barsebäck Elias Sunny walking 25 25 NA 100.84166 2.7 NV 260 106 winter ceregrowing 0-15 83 NA 204 topography 47 topography 59 car emban 97 topography 204-206 2021-03-05 11:37:00 Northern Barsebäck Elias Sunny walking 2 2 NA 67.952624 3.4 V 334 110 winter ceregrowing 0-15 60 135 40 topography 5 previous ye 10 previous ye 58 topography 207-209 2021-03-05 12:55:00 Northern Bjärred Elias Sunny walking 6 18 24 1. greylag 270.932780 3.9 NV multiple 147 without reagrowing 0-15 62 NA 30 topography 29 topography 52 topography 30 topography 210-2111-2121 2021-03-05 14:19:00 Northern Habo LjungElias Sunny walking 15 15 NA 116.89297 1.3 V 240 357 winter ceregrowing 0-15 80 NA 14 topography 61 topography 38 topography 7 tree 213-215 2021-03-05 15:32:00 Northern Flädie Elias Sunny walking 13 5 18 1. greylag 2143.69039 4.1 NV multiple bu 49 winter whegrowing 0-15 NA 285 111 topography 117 tree 98 reed aggre 142 topography 217-219 2021-03-05 16:50:00 Northern Lomma no Elias Sunny walking 26 26 NA 25.393134 3.1 NV 351 53 winter ceregrowing 0-15 NA NA 97 topography 91 topography 117 car emban 128 tree 220-2211-222-223 2021-03-08 16:43:00 Free Tullstorp Elias Sunny walking 67 67 NA 222.15659 3.5 NV multiple 242 winter whegrowing 0-15 162 NA 47 topography 82 topography 25 topography 31 topography 224-226 2021-03-09 09:59:00 Eastern Roslöv Elias Cloudy walking 2 2 NA 189.85046 1.8 S 71 110 winter whegrowing 0-15 136 NA 20 topography 74 topography 139 topography 43 topography 227-229 2021-03-09 10:29:00 Eastern Holmby Elias Cloudy walking 16 16 NA 52.162263 2 SV 233 142 winter ceregrowing 0-15 45 156 45 topography 83 topography 54 topography 39 buffert zon 230-232 2021-03-09 11:11:00 Eastern Skarhult Elias Cloudy walking 4 4 NA 99.653686 2.8 S 278 13 carrot Förvaring 0-15 75 193 39 topography 27 car emban 31 topography 66 topography 233-235 2021-03-09 11:58:00 Eastern GårdstångaElias Cloudy walking 34 34 NA 55.248694 2.5 S 330 101 winter whegrowing 0-15 48 NA 43 stenmWith 20 topography 17 topography 111 tree 236-238 2021-03-09 13:39:00 Eastern Råby Elias Cloudy walking 10 10 NA 46.807832 1 S multiple 255 without reagrowing 0-15 36 NA 165 bushes 135 topography 81 topography 141 topography 239-241 2021-03-09 15:06:00 Eastern GårdstångaElias Cloudy walking 2 2 4 1. greylag 2210.89493 2.6 V 250 254 winter cereals NA NA 121 car emban 186 topography 197 tree 244 car emban 242-244 2021-03-09 18:03:00 Northern GårdstångaElias Sunny walking 16 16 NA 86.923203 0 NA multiple. b 130 winter whe growing 0-15 50 188 178 car emban 186 topography 345 house 26 topography 245-247 2021-03-10 09:42:00 Southern Bunkeflo s Elias Sunny walking 42 42 NA 79.211596 2.6 SO multiple bu 248 winter whe growing 0-15 45 119 39 car emban 90 topography 46 topography 134 car emban 248-250 2021-03-10 10:38:00 Southern Klagshamn Elias Sunny walking 6 6 NA 93.341218 2.2 SO multiple bu 105 winter whegrowing 0-15 77 114 409 virkeshög 369 topography 41 property heWithout reWithout re 251-253 2021-03-10 11:03:00 Southern Klagshamn Elias Sunny walking 6 6 NA 101.35799 3.7 SO multiple 331 winter whegrowing 0-15 99 135 81 topographyWithout reWithout re 36 höbal 143 property he 254-256 2021-03-10 11:20:00 Southern Klagshamn Elias Sunny walking 2 2 NA 96.342891 4 SO between 9 218 winter whegrowing 0-15 70 168 65 property he 282 property he 496 grustag 813 tree 257-259 2021-03-10 11:50:00 Southern Klagshamn Elias Sunny walking 16 29 45 1. greylag 2162.08704 4.7 SO NA 70 raps growing 0-15 170 222 168 topography 160 house 40 car emban 81 buffert zon 260-262 2021-03-10 14:39:00 Southern Bröddarp Elias Sunny walking 11 250 261 simultaneo149.46830 5.4 SO multiple 202 Utgrävd baNA 0-15 NA NA 3 topography 17 topography 41 topography 4 topography 263-265 2021-03-10 14:58:00 Southern Bröddarp Elias Sunny walking 2 2 NA 104.94998 3.9 SO 48 137 åkerholme growing 15-30 85 253 73 topography 10 tree 56 topography 216 car emban 266-268 2021-03-10 15:55:00 Southern Steglarp Elias Sunny walking 36 3 8 47 1. greylag 2101.97708 5.4 SO 80 to 140 34 winter cereals 100 179 14 topography 11 topography 45 topography 85 topography 269-271 2021-03-10 16:46:00 Southern Vellinge vä Elias Sunny walking 2 2 NA 154.65707 4.6 SO 110 180 winter cereals 98 214 212 virkeshög 101 topographyWithout reWithout re 201 property he 272-274 2021-03-10 17:11:00 Southern Vellinge vä Elias Sunny walking 9 9 NA 114.00637 3.8 SO 153 61 winter wheat 89 168 144 topography 289 house Without reWithout re 30 topography 275-277 2021-03-12 09:23:00 Northern Norra LomElias Cloudy walking 4 29 33 1. greylag 270.811367 5.8 V 226 154 winter ceregrowing 0-15 60 NA 84 bushes 63 topography 44 topography 81 car emban 278-280 2021-03-12 09:45:00 Northern Norra LomElias Cloudy walking 9 9 NA 145.35615 5.7 V 281 247 winter wheat 135 NA 84 tree 100 topography 65 topography 83 car emban 281-283 2021-03-12 10:22:00 Northern Lilla LommElias Cloudy walking 14 14 NA 94.972531 2.2 V 305 41 winter ceregrowing 0-15 86 151 141 topography 21 tree 162 tree 147 topography 284-286 2021-03-12 10:59:00 Northern Lilla LommElias Cloudy walking 2 2 NA 167.70978 5 SV 222 175 winter cereals NA 177 Without reWithout re 176 house NA NA 170 car emban 288-290 2021-03-12 11:33:00 Northern Borgeby Elias Cloudy walking 2 2 NA 146.47965 5.1 V 294 18 bare soil NA 0 NA 174 53 topography 55 topography 273 tree 262 tree 291-293 2021-03-12 13:26:00 Northern StenbockslElias Cloudy walking 7 7 NA 194.03693 6.5 SV 241 190 winter rapegrowing 0-15 183 320 61 tree NA NA 30 topography 74 car emban 294-296 2021-03-12 14:20:00 Northern Barsebäck Elias Cloudy andwalking 46 46 NA 104.35626 1.5 V multiple 10 winter ceregrowing 0-15 91 126 89 previous ye 34 previous ye 67 car emban 45 topography 297-299 2021-03-12 14:40:00 Northern LöddeköpiElias Cloudy walking 2 2 NA 101.05715 3.1 V 282 353 winter whegrowing 0-15 91 118 267 car emban 98 car emban 126 car embanWithout reWithout re 300-302 2021-03-12 15:36:00 Northern Löddeköpi Elias Cloudy walking 9 9 NA 78.870394 4.4 SV 231 323 winter wheat 83 205 164 topography 224 tree 113 previous ye 173 reed aggre 303-305 2021-03-12 16:25:00 Northern Västra HobElias Cloudy walking 38 6 44 simultaneo98.832804 4.8 SV 265 302 winter wheat 105 250 158 tree 183 topography 87 topography 23 topography 306-308 2021-03-12 16:53:00 Northern Stångby ky Elias Cloudy walking 20 20 NA 124.13252 3.9 S 145 25 winter whegrowing 0-15 NA 158 183 topographyWithout reWithout re 213 tree 748 house 309-311 2021-03-15 10:07:00 Free Högestad Elias Cloudy andwalking 3 3 NA 133.96861 5 S multiple 318 winter whegrowing 0-15 111 208 14 topography 131 topography 190 topography 50 topography 313-315 2021-03-15 10:56:00 Free KöpingebroElias Cloudy walking 2 2 NA 45.918386 1.9 S 150 61 Hästbete growing 0-15 38 76 5 reed aggre 60 topography 64 reed aggre 5 reed aggre 316-318 2021-03-15 11:54:00 Free Ystad norraElias Cloudy walking 1 1 NA 75.496022 1.8 S 33 97 ley growing 0-15 76 98 24 car emban 153 topography 81 topography 42 topography 319-321 2021-03-15 12:25:00 Free Balkåkra Elias Cloudy walking 2 2 NA 71.173380 2.1 S 326 163 winter whegrowing 0-15 62 81 32 topography 46 topography 12 bushes 54 topography 322-324 2021-03-15 12:51:00 Free Snårestad Elias Cloudy walking 2 2 NA 103.96790 3.1 S 348 263 Mkt klent wgrowing 0-15 92 135 28 tree 54 topography 18 topography 180 topography 325-327 2021-03-15 13:17:00 Free Sjörups kyrElias Cloudy walking 7 7 NA 111.80531 3.4 S 274 169 winter whegrowing 0-15 97 161 114 topography 16 topography 19 topography 16 topography 328-330 2021-03-15 14:03:00 Free Skivarp Elias Cloudy walking 5 5 NA 164.73848 2.6 S Circulating 67 winter whegrowing 0-15 175 264 Without reWithout re 496 house 115 car emban 326 tree 331-333 2021-03-15 14:20:00 Free Skivarp Elias Cloudy walking 37 37 NA 116.91853 1.6 S 280 300 winter ceregrowing 0-15 84 191 388 house 125 housebil 94 car embanWithout reWithout re 334-336 2021-03-15 15:35:00 Free Klörup Elias Cloudy walking 22 22 NA 98.907639 1.5 S 180 196 winter whegrowing 0-15 90 147 79 car emban 81 topography 84 topography 68 topography 337-339 2021-03-16 10:00:00 Eastern Yddingesjö Elias Cloudy walking 7 32 39 1. greylag 2140.66839 3.1 N 352 310 winter whegrowing 0-15 116 NA 55 topography 209 topography 32 tree 132 topography 340-342 2021-03-16 10:30:00 Eastern Hyby Elias Cloudy walking 2 2 NA 89.277243 3.6 N 132 114 winter whegrowing 0-15 75 133 Without reWithout reWithout reWithout re 79 topography 349 topography 343-345 2021-03-16 10:53:00 Eastern Beden Elias Cloudy walking 6 6 NA 96.131472 2.9 N 299 159 winter whegrowing 0-15 84 165 202 topography 19 topography 79 tree 246 house 346-348 2021-03-16 11:32:00 Eastern Assartorp Elias Cloudy walking 16 16 NA 44.695165 3.2 N 14 301/290 without reagrowing 0-15 33 136 51 topography 87 topography 88 topography 99 topography 349-351 2021-03-16 12:01:00 Eastern Assartorp Elias Cloudy walking 2 2 NA 49.602578 1.6 NO 256 190 without reagrowing 0-15 53 97 62 topography 55 topography 107 tree 59 tree 352-354 2021-03-16 12:27:00 Eastern Genarp Elias Cloudy walking 2 2 NA 77.231169 1.3 NV 303 1 winter whegrowing 0-15 65 103 17 property he 199 topography 89 car emban 250 stenmWith 355-357 2021-03-16 12:55:00 Eastern UgglarpssjöElias Cloudy walking 12 12 NA 135.99672 2.6 NO 172 175 ley growing 0-15 110 233 160 tree fence 82 topography 54 tree 138 car emban 358-360 2021-03-16 13:50:00 Eastern Alberta Elias Cloudy walking 7 7 NA 133.52654 2.9 N 144 & 175 122 winter whegrowing 0-15 81 260 301 house Without reWithout re 204 topography 196 car emban 361-363 2021-03-17 09:33:00 Southern Bunkeflo s Elias Sunny walking 20 20 NA 30.007789 1.5 NV 186 241 bare soil NA 0 27 114 623 house 140 tree 106 previous ye 111 villastaket 366-368 2021-03-17 10:58:00 Southern Västra Kär Elias Cloudy walking 2 2 NA 139.35080 1.5 NV 120-190 165 winter whegrowing 0-15 122 188 41 topography 29 bush 9 topography 99 bush 369-371 2021-03-17 12:16:00 Southern Rängs SandElias Cloudy walking 63 63 NA 173.58486 1.8 NV 118 117 ley & bare soil 173 328 33 topography 39 previous yeWithout reWithout reWithout reWithout re 372-374 2021-03-17 13:02:00 Southern Trelleborg Elias Cloudy walking 17 17 NA 79.705359 2.2 Ö Multiple. b 331 ley growing 0-15 69 NA 182 topography 326 tree 230 house 78 topography 375-377 2021-03-17 14:14:00 Southern Västra Vär Elias Sunny walking 4 4 NA 118.76064 0.5 SV multiple 108 winter whegrowing 0-15 94 155 357 house 14 topography 107 buffert zon 119 tree 378-380 2021-03-17 15:20:00 Southern Gessie villaElias Cloudy walking 1040 1040 NA 53.311507 2.6 NO 298 236 without reagrowing 0-15 50 156 39 buffert zon 125 buffert zon 420 reed aggre 28 buffert zon 381-383 2021-03-17 15:58:00 Southern Gessie villaElias Cloudy walking 14 14 NA 76.587158 0 NA 351 104 winter wheat 57 136 253 previous ye 366 NA 197 horse fenc 157 previous ye 384-386 2021-03-17 17:04:00 Southern Klagshamn Elias Cloudy walking 5 5 NA 43.724060 1.8 SV 165 110 winter whegrowing 0-15 33 135 330 house 247 villastaket 272 reed aggre Without reWithout re 387-389 2021-03-18 09:54:00 Northern Alnarp Elias Sunny walking 6 6 NA 167.81201 2.2 NV 108 179 winter whegrowing 0-15 163 NA 48 tree 60 topography 622 house 134 house 393-395 2021-03-18 12:51:00 Northern Svalöv nor Elias Sunny walking 23 23 NA 50.413361 3.3 NO multiple. b 207 winter wheat & bare soil 41 87 127 house Without reWithout re 155 topography 72 topography 396-398 2021-03-18 15:07:00 Northern StenbockslElias Sunny walking 5 5 NA 72.511689 4.3 NV 184 61 winter whegrowing 0-15 NA 117 320 tree 72 topography 34 halmtäckn Without reWithout re 399-401 2021-03-18 15:27:00 Northern StenbockslElias Sunny walking 4 4 NA 102.18914 <0.5 NV NA 303 winter ceregrowing 0-15 80 330 48 tree 60 topogra 272 topograph 88 topography 402-404 2021-03-18 16:05:00 Northern Barsebäck Elias Sunny walking 17 (1) Fanns i flocken initialt men dr 2 19 1. greylag 276.947957 3.1 N 141 118 winter whegrowing 0-15 70 104 103 car emban 25 reed aggre 25 topography 21 topography 407-409 2021-03-18 17:27:00 Northern FWithout rElias Sunny walking 12 12 NA 90.404323 4.4 NV multiple bu 165 winter whegrowing 0-15 92 166 41 topography 69 topographyWithout reWithout re 124 topography 410-412 2021-03-19 09:18:00 Free KristineberElias Sunny walking 4 4 NA 58.014599 NA NA 85 156 winter ceregrowing 0-15 67 111 115 car emban100 topogrtopography 129 järnvägsva 286 topography 413-415 2021-03-19 09:46:00 Free Arrie Elias Sunny walking 2 2 NA 193.44422 2 NO 100 203 winter whegrowing 0-15 186 NA 91 topography 30 topography 397 tree 21 buffert zon 416-418 2021-03-19 10:26:00 Free Västra IngeElias Sunny walking 2 2 NA 165.97426 4.1 NO 140 229 winter whegrowing 0-15 149 NA 185 topography 103 previous ye 111 topography 66 topography 419-421 2021-03-19 12:08:00 Free SmygehamElias Sunny walking 6 (1) Står kvar på 56 m från WP3 6 NA 55.134620 1.8 NO 20 39 without reagrowing 0-15 60 NA 36 reed aggre 80 topograph 336 house 47 topography 422-424 2021-03-19 13:35:00 Free NäsbyholmElias Sunny walking 8 68 76 simultaneo92.215028 2.1 NO 85 NA without reagrowing 0-15 62 73 538 tree 34 reed aggre 62 car emban 28 topography 425-427 2021-03-19 14:15:00 Free SkWithout Elias Sunny walking 2 2 4 1. greater w84.651490 2.9 NO 315 210 winter whegrowing 0-15 NA NA 40 topography 6 topography 62 topography 58 topography 428-430 2021-03-19 15:13:00 Free SvaneholmElias Sunny walking 5 5 NA 57.681633 3.9 NO 103 185 without reach & odefinierat p805 53 103 323 tree 13 topography 43 previous ye 90 car emban 431-433 2021-03-19 15:46:00 Free Janstorp Elias Sunny walking 5 5 NA 73.923892 1.8 NO 210-280 285 ley growing 0-15 65 NA 91 topography 51 topography 14 topography 22 previous ye 434-436 2021-03-19 16:20:00 Free LemmeströElias Sunny walking 2 2 NA 67.550189 1.2 N 77 61 winter whegrowing 0-15 65 203 38 topography16 topogratopography 24 topography 160 jordhög 437-439 2021-03-21 10:38:00 Eastern GårdstångaElias Sunny walking 14 14 NA 158.58106 3.8 NV 235 233 winter whegrowing 0-15 157 161 88 car emban 137 topography 289 tree 324 tree 440-442 2021-03-21 11:38:00 Eastern Harlösa Elias Sunny walking 2 2 NA 64.513189 3.5 NO 200 272 without reagrowing 0-15 60 147 19 car emban 38 topography 280 tree 113 topography 443-445 2021-03-21 13:10:00 Eastern Silvåkra Elias Sunny walking 2 2 NA 44.947829 3 NV 275 345 without reagrowing 0-15 42 130 171 villastaket 17 car embanWithout reWithout re 125 tree 446-449 2021-03-21 14:04:00 Eastern Harlösa Elias Sunny walking 53 53 NA 73.955821 5.5 NV 234 162 without reagrowing 0-15 61 NA 30 topography 41 topography 229 topography 201 topography 450-452 2021-03-21 14:58:00 Eastern leyby Elias Sunny walking 30 2 32 1. greylag 2178.60748 5 NV 295 6 winter whegrowing 0-15 149 373 264 topography 87 topography 199 topographyNA NA 453-455 2021-03-21 15:27:00 Eastern Alberta Elias Sunny walking 7 7 NA 65.872622 6 NO 148 & 197 109 winter whegrowing 0-15 63 95 116 topographyNA NA 68 träskjul 60 halmbal 456-458 2021-03-22 10:02:00 Southern Klagshamn Elias Cloudy walking 14 14 NA 76.790005 2.2 S 214 88 winter ceregrowing 0-15 69 NA 90 topography 34 bush Without reWithout re 140 villastaket 459-461 2021-03-22 11:15:00 Southern Maglarp Elias Cloudy walking 4 4 NA 42.815002 2.5 S 194 132 bare soil, wNA 39 NA 68 reed aggre 107 topography 417 car emban 30 topography 462-464 2021-03-22 12:37:00 Southern Arrie Elias Cloudy walking 5 5 NA 47.633761 2.2 S 148 158 winter ceregrowing 0-15 45 179 164 topography 119 tree 50 topography 112 stenmWith 465-468 2021-03-22 13:40:00 Southern Östra GrevElias Cloudy walking 2 2 NA 53.646219 2.4 S 61 40 winter rapegrowing 0-15 57 NA 60 topography 40 reed aggre 167 topography 66 reed aggre 469-471 2021-03-22 14:07:00 Southern Arrie Elias Cloudy walking 4 4 NA 54.978805 4.7 V 210 159 winter rapegrowing 0-15 NA NA 60 previous ye 32 topography 46 car emban 53 car emban 472-474 2021-03-22 15:10:00 Southern Lockarp Elias Cloudy walking 2 2 NA 49.360248 NA NA 214 15 winter whegrowing 0-15 47 106 84 tree 31 reed aggre 134 previous ye 261 property he 475-477 2021-03-22 15:29:00 Southern Glostorp Elias Cloudy walking 2 2 NA 54.029151 3.9 V 299 222 hästbete growing 0-15 56 110 93 topographyWithout reWithout re 98 horse fenc 156 topography 478-480 2021-03-22 15:57:00 Southern Gessie villaElias Cloudy walking 9 9 NA 43.232943 1.6 SV NA 129 ley med stugrowing 15-30 44 104 61 jordhög 228 bush 90 property he 126 tree 481-483 2021-03-22 16:18:00 Southern Pile Elias Cloudy walking 5 580 585 1. barnacle121.43416 3.6 SV multiple 57 winter whegrowing 0-15 110 219 302 house Without reWithout re 102 tree 484 tree 484-486 2021-03-23 09:31:00 Northern Bernstorp Elias Cloudy walking 390 390 NA 38.466366 1.7 V multiple bu 343 winter whegrowing 0-15 38 165 122 reed aggre 314 house 47 reed aggre 134 reed aggre 487-489 2021-03-23 11:29:00 Northern Nybo Elias Cloudy walking 5 5 NA 62.455168 1.5 V 214 261 without reagrowing 0-15 59 134 30 villastaket 25 tree 64 tree 55 topography 490-492 2021-03-23 12:21:00 Northern Flädie Elias Cloudy walking 185 185 NA 162.08566 0.5 V multiple. m 132 winter ceregrowing 0-15 158 NA 114 tree 117 topography 103 tree 61 vass 493-495 2021-03-23 13:31:00 Northern Bjärred södElias Cloudy walking 4 4 NA 45.902585 2.2 SV 168 170 winter rapegrowing 15-30 40 92 502 house Without reWithout re 123 previous ye 205 villastaket 497-499 2021-03-23 14:34:00 Northern Bjärred östElias Cloudy walking 4 4 NA 160.25084 0 NA 53 294 winter whegrowing 15-30 157 295 106 höbal 248 topography 61 car emban 57 previous ye 500-502 2021-03-23 15:29:00 Northern Alnarp Elias Cloudy walking 5 430 435 1. barnacle76.578492 2.9 SV multiple. b 358 winter whegrowing 15-30 69 163 31 topography 106 buffert zon 75 topography 336 tree 503-505 2021-03-23 15:54:00 Northern Lomma Elias Cloudy walking 2 2 NA 32.646367 2.6 SV 240 143 winter whegrowing 15-30 30 103 bush 172 topography 76 topography 230 tree 506-508 2021-03-23 17:00:00 Northern Bjärred Elias Cloudy walking 6 6 NA 84.856252 1.7 SV 190-280 241 bare soil NA 0 80 232 208 property he 171 tree 45 tree 36 tree 509-511 2021-03-23 17:47:00 Northern LöddeköpiElias Cloudy walking 11 9 20 simultaneo88.744141 1.4 SV 160 82 bare soil & NA 0-15 86 146 25 topography 162 vägräcke 85 topography 12 topography

Appendix 2

The most recently systematically collected data of FID among LGB in Scania, were collected between 2020-11-11 and 2021-03-14. These data were collected on the initiative by my supervisors in the Kristianstad municipality. The data are yet unpublished, but with their permission, they can be used for quick comparisons to the data collected for this thesis. An advantage in the comparison is that the methods are the same for both datasets. In the discussion, the results from Kristianstad will be contrasted to the results of this thesis.

In total 201 scaring trials were conducted in the Kristianstad dataset, and 143 of these could be categorized as T-geese constellations, see figure 1. The overall mean for T-geese was 127.80 meters and the median was 118.34 meters. As discerned by figure 2, FID-values were not normally distributed.

Figure 1. Flock constellations during scaring trials in the Kristianstad municipality. Since these scaring trials included all LGB, the constellation “other” is also included. In total, 201 scaring trials were performed. 143 of these regarded T-geese (i.e., barnacle, greylag and mixed flocks).

Figure 2. Histogram when T-geese are selected in the dataset of scaring trials in Kristianstad.

Comparisons between constellations are shown in figure 3, and since greylag and mixed constellations exhibited unnormal distribution, a Kruskal-Wallis test was run to test differences in FID between the constellations. The Kruskal-Wallis test produced a p-value of 0.4985, which refuted the potential of significant inferential differences between the constellations.

Figure 3. FID compared between constellations. Whiskers are set as 1.5*Inter quartile range. Values outside whiskers are considered as outliers and depicted as dots.

Appendix 3

Below are the linear models of the multiple regression showed with untransformed FID-values.

Table 1. Coefficients of the multiple linear regression describing greylag goose flocks. Unransformed FID-values. Coefficient Estimate Std. error Intercept 131.663 39.531 Distance to nearest 0.287 0.088 object Ordinal date -0.713 0.527 Individuals 0.553 0.29

Table 2. Coefficients of the multiple linear regression describing barnacle goose flocks. Untransformed FID-values. Coefficient Estimate Std. error Intercept 25.589 193.116 Distance to nearest -0.023 0.257 object Ordinal date 0.451 2.354 Individuals 0.028 0.037

Table 3. Coefficients of the multiple linear regression describing mixed flocks. UntransformedFID-values. Coefficient Estimate Std. error Intercept 101.81 70.504 Distance to nearest 0.408 0.165 object Ordinal date -0.056 1.015 Individuals -0.022 0.036

Figure 1. Linear model for all species using untransformed FID-values. Regression lines are based on the equations below which in turn are taken from table one to three.

Barnacle: y(FID)= -0.0232*Distance to nearest object + 0.02786 *Mean number of individuals + 0.45155*Mean ordinal date + 25.58930 (Intercept)

Greylag: y(FID)= 0.28669*Distance to nearest object + 0.55359*Mean number of individuals + -0.71264*Mean ordinal date + (131.66291) (Intercept)

Mixed: y(FID): 0.40783*Distance to nearest object + -0.02244*Mean number of individuals + 0.05646*Mean ordinal date + (101.80599) (Intercept)

Appendix 4

Below are the coordinates for all waypoints, WP1 are also included, and thus it’s usually just each third ID that constitutes an FID value. Sometimes cancelled scaring trials or data collection errors imply longer jumps between FIDs to obtain the FIDs, compare with the FID column in appendix 1. For illustrative purposes, the first FID 11 FID-values are written out explicitly.

ID Lat Lon ele time POINT_X POINT_Y Name Pythagora’s FID

(WGS84) (WGS84) (RT90) (RT90) theorem 1 55,4567 13,00165 2,890549 2021-02- 1322485 6151019 1

12T09:41:18Z 2 55,45649 13,00149 4,604938 2021-02- 1322474 6150995 2

12T09:44:39Z 3 55,45559 13,00103 4,483433 2021-02- 1322441 6150897 3 103,8517 103,8517

12T09:46:19Z 4 55,51114 12,93719 1,447363 2021-02- 1318660 6157243 4 7386,742

12T11:06:06Z 5 55,51072 12,93543 0,489635 2021-02- 1318547 6157201 5 120,779

12T11:09:19Z 6 55,51035 12,93469 -0,40465 2021-02- 1318498 6157161 6 62,73842 62,73842

12T11:10:35Z 7 55,5153 12,93546 1,11306 2021-02- 1318570 6157710 7 553,4468

12T11:30:17Z 8 55,51589 12,93572 1,298123 2021-02- 1318589 6157776 8 68,435

12T11:33:00Z 9 55,51697 12,93658 1,123736 2021-02- 1318648 6157893 9 131,5699 131,5699

12T11:38:53Z 10 55,73709 13,34719 17,41227 2021-02- 1345442 6181394 10 35639,42

12T12:32:25Z 11 55,7373 13,3461 16,32608 2021-02- 1345374 6181420 11 72,52994

12T12:35:04Z 12 55,73785 13,34461 18,6179 2021-02- 1345283 6181484 12 111,5269 111,5269 12T12:37:45Z

13 55,72995 13,34043 21,67617 2021-02- 1344989 6180614 13 918,0171

12T13:04:08Z 14 55,73002 13,34019 23,33881 2021-02- 1344974 6180623 14 17,0208

12T13:07:08Z 15 55,73026 13,33923 20,60979 2021-02- 1344914 6180652 15 66,51753 66,51753

12T13:09:25Z 16 55,73535 13,3697 20,65637 2021-02- 1346848 6181150 16 1996,869

12T13:32:51Z 17 55,73484 13,36973 19,71408 2021-02- 1346848 6181093 17 56,7053

12T13:35:55Z 18 55,73433 13,36974 17,27806 2021-02- 1346847 6181037 18 56,69253 56,69253

12T13:38:02Z 19 55,65065 13,05591 9,152039 2021-02- 1326772 6172463 25 21829,07

18T09:38:51Z 20 55,65079 13,05632 7,73597 2021-02- 1326798 6172478 26 30,20731

18T09:44:10Z 21 55,65117 13,05698 8,697367 2021-02- 1326841 6172519 27 59,55539 59,55539

18T09:46:16Z 22 55,73063 13,04194 13,73142 2021-02- 1326248 6181398 28 8899,434

18T10:34:43Z 23 55,73028 13,04175 13,51948 2021-02- 1326235 6181359 29 41,38508

18T10:40:07Z 24 55,72866 13,04162 14,12552 2021-02- 1326219 6181180 30 180,1463 180,1463

18T10:43:48Z 25 55,74231 13,03021 14,53399 2021-02- 1325564 6182728 31 1680,897

18T12:09:00Z 26 55,74231 13,03022 12,59155 2021-02- 1325564 6182727 32 0,706752

18T12:14:18Z 27 55,74261 13,03064 10,24581 2021-02- 1325592 6182759 33 42,51999 42,51999

18T12:15:42Z 28 55,74232 13,03016 10,78886 2021-02- 1325561 6182729 34 43,57692

18T12:23:33Z 29 55,74233 13,03017 11,9274 2021-02- 1325561 6182729 35 0,550956

18T12:25:40Z 30 55,74242 13,03423 10,52817 2021-02- 1325816 6182730 36 255,1645 255,1645

18T12:30:58Z 31 55,7546 13,05286 13,08635 2021-02- 1327040 6184038 37 1791,447

18T15:11:27Z 32 55,7546 13,05286 14,54801 2021-02- 1327039 6184038 38 0,54831 18T15:13:11Z

33 55,75479 13,05434 14,07591 2021-02- 1327133 6184055 39 95,3756 95,3756

18T15:15:35Z 34 55,73413 13,34468 23,93232 2021-02- 1345272 6181070 40 18382,9

19T09:03:26Z 35 55,73413 13,34468 23,82601 2021-02- 1345272 6181070 41 0,448763

19T09:06:36Z 36 55,73434 13,34203 0 2021-02- 1345107 6181100 42 168,0579

19T09:13:59Z 37 55,73175 13,37694 17,22404 2021-02- 1347289 6180733 43 2212,41

19T09:54:45Z 38 55,73175 13,37683 17,41389 2021-02- 1347282 6180734 44 6,947574

19T10:02:06Z 39 55,73257 13,38068 16,00521 2021-02- 1347526 6180817 45 258,3565

19T10:09:19Z 40 55,72894 13,48179 26,80883 2021-02- 1353862 6180194 46 6366,409

19T11:45:13Z 41 55,7286 13,48111 22,4345 2021-02- 1353819 6180158 47 56,94677

19T11:48:02Z 42 55,72772 13,4794 18,69348 2021-02- 1353708 6180064 48 145,2554

19T11:53:54Z 43 55,73103 13,4796 27,16833 2021-02- 1353733 6180432 49 368,2377

19T12:27:22Z 44 55,73162 13,47953 30,07014 2021-02- 1353731 6180498 50 65,9401

19T12:30:04Z 45 55,73387 13,47989 43,7076 2021-02- 1353761 6180747 51 251,1994

19T12:36:37Z 46 55,68512 13,42426 35,45119 2021-02- 1350082 6175441 52 6456,721

19T13:24:24Z 47 55,68491 13,42425 36,29904 2021-02- 1350080 6175417 53 24,0663

19T13:33:59Z 48 55,68553 13,41834 29,86811 2021-02- 1349711 6175500 54 378,3728

19T13:44:00Z 49 55,51632 12,93494 21,4938 2021-02- 1318542 6157825 55 35832

22T11:22:12Z 50 55,51634 12,93508 20,72131 2021-02- 1318550 6157827 56 8,937145

22T11:23:15Z 51 55,51686 12,9372 18,91805 2021-02- 1318687 6157880 57 146,2484

22T11:27:14Z 52 55,50801 12,93921 6,564308 2021-02- 1318773 6156889 58 994,6051 22T11:55:08Z

53 55,50808 12,9385 4,909363 2021-02- 1318728 6156899 59 45,25305

22T11:57:19Z 54 55,50814 12,93743 3,05024 2021-02- 1318661 6156909 60 68,15073

22T12:00:32Z 55 55,50814 12,93743 1,904266 2021-02- 1318661 6156908 61 0,941736

22T12:07:28Z 56 55,50797 12,93614 0,964378 2021-02- 1318579 6156892 62 84,16654

22T12:10:38Z 57 55,50788 12,93488 0,254358 2021-02- 1318499 6156887 63 80,21738

22T12:12:36Z 58 55,51365 12,97625 11,91363 2021-02- 1321137 6157420 64 2692,112

22T12:39:00Z 59 55,51327 12,97718 13,19948 2021-02- 1321195 6157376 65 72,12055

22T12:40:44Z 60 55,51297 12,978 12,87437 2021-02- 1321245 6157340 66 62,13438

22T12:42:55Z 61 55,42971 12,9793 -1,64864 2021-02- 1320950 6148073 67 9272,108

22T13:59:39Z 62 55,43031 12,97873 -0,28658 2021-02- 1320916 6148141 68 75,94127

22T14:05:17Z 63 55,43089 12,97783 -1,4018 2021-02- 1320862 6148208 69 86,47619

22T14:06:29Z 64 55,41632 13,01338 0,732262 2021-02- 1323047 6146496 70 2775,796

22T14:26:16Z 65 55,41642 13,01227 0,792366 2021-02- 1322977 6146509 71 71,33001

22T14:28:11Z 66 55,41653 13,00956 -0,64568 2021-02- 1322806 6146529 72 172,0838

22T14:31:51Z 67 55,40264 13,01968 -2,70144 2021-02- 1323384 6144957 73 1674,736

22T14:51:09Z 68 55,40252 13,02013 -3,74203 2021-02- 1323412 6144943 74 31,26372

22T14:52:10Z 69 55,40182 13,02321 -4,96451 2021-02- 1323604 6144857 75 210,2869

22T14:56:02Z 70 55,57638 13,17275 17,08264 2021-02- 1333810 6163912 82 21616,56

23T08:58:38Z 71 55,57588 13,17221 17,16873 2021-02- 1333773 6163858 83 65,24946

23T09:00:14Z 72 55,57692 13,17316 18,4055 2021-02- 1333838 6163972 84 130,7585 23T09:10:39Z

73 55,58311 13,29339 33,30159 2021-02- 1341442 6164380 85 7615,098

23T09:29:55Z 74 55,58254 13,29383 34,09015 2021-02- 1341468 6164315 86 69,24333

23T09:32:17Z 75 55,58202 13,29494 35,53235 2021-02- 1341536 6164255 87 91,09544

23T09:33:45Z 76 55,60467 13,38365 29,20879 2021-02- 1347215 6166577 88 6136,054

23T10:05:01Z 77 55,60477 13,38367 28,20224 2021-02- 1347217 6166587 89 10,81081

23T10:06:08Z 78 55,6058 13,38387 27,95356 2021-02- 1347234 6166702 90 116,2496

23T10:08:08Z 79 55,60434 13,38692 30,84442 2021-02- 1347420 6166533 91 251,7603

23T10:22:09Z 80 55,60486 13,38705 31,49016 2021-02- 1347430 6166590 92 58,16625

23T10:26:46Z 81 55,60583 13,38721 30,86178 2021-02- 1347445 6166698 93 108,9419

23T10:29:55Z 82 55,51798 13,44276 73,65074 2021-02- 1350611 6156800 94 10392,04

23T11:05:01Z 83 55,51799 13,44276 73,57692 2021-02- 1350611 6156802 95 1,81082

23T11:06:03Z 84 55,51916 13,44459 76,03682 2021-02- 1350730 6156928 96 173,353

23T11:08:42Z 85 55,44905 13,72707 31,98962 2021-02- 1368333 6148554 97 19492,7

23T13:33:41Z 86 55,44985 13,7275 32,81448 2021-02- 1368363 6148643 98 93,40907

23T13:36:30Z 87 55,4503 13,7279 33,88223 2021-02- 1368390 6148692 99 55,71223

23T13:38:12Z 88 55,44694 13,71077 24,57799 2021-02- 1367295 6148351 100 1146,926

23T13:55:06Z 89 55,4464 13,71135 22,48768 2021-02- 1367330 6148289 101 70,89087

23T13:58:19Z 90 55,44584 13,71138 25,13479 2021-02- 1367330 6148227 102 62,05181

23T13:59:45Z 91 55,43121 13,55398 16,22344 2021-02- 1357320 6146911 103 10095,84

23T14:38:02Z 92 55,4309 13,55419 15,20625 2021-02- 1357332 6146876 104 37,09805 23T14:40:02Z

93 55,42996 13,55532 13,35725 2021-02- 1357400 6146768 105 127,1703

23T14:43:20Z 94 55,44512 13,46584 57,64143 2021-02- 1351795 6148643 106 5910,63

23T15:15:39Z 95 55,44404 13,46651 54,55748 2021-02- 1351833 6148522 107 127,2192

23T15:19:39Z 96 55,44299 13,46656 54,00355 2021-02- 1351832 6148404 108 117,6132

23T15:21:54Z 97 55,43039 13,4337 51,42104 2021-02- 1349705 6147072 109 2509,339

23T15:46:21Z 98 55,4299 13,43456 52,4895 2021-02- 1349758 6147017 110 76,61002

23T15:49:08Z 99 55,42929 13,43572 50,69029 2021-02- 1349829 6146945 111 100,5745

23T15:51:19Z 100 55,70829 13,04022 3,119473 2021-02- 1326040 6178917 112 39850,71

24T09:23:05Z 101 55,70705 13,04144 -1,41004 2021-02- 1326112 6178776 113 158,4464

24T09:29:47Z 102 55,70612 13,04195 -2,3458 2021-02- 1326140 6178670 114 108,8606

24T09:33:33Z 103 55,7467 13,04745 16,29694 2021-02- 1326665 6183172 115 4532,735

24T10:14:25Z 104 55,74583 13,04521 11,14381 2021-02- 1326521 6183082 116 170,6911

24T10:17:53Z 105 55,74504 13,04342 9,774 2021-02- 1326405 6182998 117 142,6251

24T10:21:06Z 106 55,76748 12,96441 5,708155 2021-02- 1321548 6185695 118 5555,293

24T12:17:40Z 107 55,76753 12,96447 4,98025 2021-02- 1321552 6185702 119 7,256293

24T12:18:38Z 108 55,76871 12,96635 5,479695 2021-02- 1321675 6185827 120 176,0602

24T12:21:55Z 109 55,76669 12,95863 7,365985 2021-02- 1321182 6185622 121 534,3513

24T12:40:31Z 110 55,76668 12,95865 6,94461 2021-02- 1321183 6185622 122 1,363139

24T12:41:31Z 111 55,76609 12,95944 4,013562 2021-02- 1321230 6185554 123 82,64678

24T12:43:15Z 112 55,7674 12,9724 5,804142 2021-02- 1322049 6185666 124 826,3259 24T13:58:13Z

113 55,76781 12,97264 4,80606 2021-02- 1322066 6185712 125 48,74746

24T14:01:12Z 114 55,76901 12,97332 5,940853 2021-02- 1322114 6185843 126 140,0789

24T14:06:07Z 115 55,74226 13,03024 10,70723 2021-02- 1325565 6182722 127 4653,519

24T14:51:44Z 116 55,7425 13,03045 7,897609 2021-02- 1325579 6182748 128 29,63009

24T14:53:27Z 117 55,74293 13,03092 6,586313 2021-02- 1325611 6182795 129 56,44415

24T14:54:53Z 118 55,73508 13,04643 5,754436 2021-02- 1326550 6181882 130 1309,763

24T15:25:45Z 119 55,73764 13,04496 5,935586 2021-02- 1326469 6182171 131 300,0993

24T15:38:02Z 120 55,73809 13,04559 5,645923 2021-02- 1326511 6182219 132 64,14652

24T15:41:45Z 121 55,74721 13,32596 18,39747 2021-02- 1344149 6182568 133 17641,77

26T09:01:07Z 122 55,74804 13,32594 16,30956 2021-02- 1344151 6182660 134 92,09588

26T09:03:58Z 123 55,74959 13,32567 13,45693 2021-02- 1344140 6182833 135 173,7675

26T09:06:59Z 124 55,78868 13,36472 55,84377 2021-02- 1346745 6187096 136 4995,762

26T09:50:09Z 125 55,78848 13,36588 57,40345 2021-02- 1346817 6187071 137 76,18311

26T09:52:14Z 126 55,78813 13,36835 58,17818 2021-02- 1346970 6187027 138 159,6445

26T09:55:13Z 127 55,76502 13,31897 18,97493 2021-02- 1343781 6184565 139 4028,945

26T11:03:52Z 128 55,76597 13,31962 17,98999 2021-02- 1343826 6184670 140 113,94

26T11:06:08Z 129 55,76717 13,32021 21,38251 2021-02- 1343868 6184802 141 138,8293

26T11:08:27Z 130 55,7284 13,46738 18,90479 2021-02- 1352955 6180165 142 10202,51

26T11:50:45Z 131 55,72865 13,46719 20,75647 2021-02- 1352944 6180193 143 30,02751

26T11:52:38Z 132 55,72902 13,46683 17,05538 2021-02- 1352923 6180235 144 46,83709 26T11:54:43Z

133 55,72325 13,5072 34,01543 2021-02- 1355437 6179509 145 2616,754

26T14:12:09Z 134 55,72162 13,50482 21,00359 2021-02- 1355282 6179332 146 235,3846

26T14:17:45Z 135 55,72111 13,5032 21,73294 2021-02- 1355178 6179278 147 116,6887

26T14:19:53Z 136 55,72508 13,50097 35,46544 2021-02- 1355052 6179725 148 463,3657

26T14:39:14Z 137 55,72444 13,50064 28,76172 2021-02- 1355030 6179655 149 73,601

26T14:42:48Z 138 55,72331 13,49853 18,53625 2021-02- 1354892 6179533 150 183,2425

26T14:46:09Z 139 55,72858 13,48125 19,46921 2021-02- 1353827 6180156 151 1234,11

26T15:05:09Z 140 55,72845 13,48107 17,35359 2021-02- 1353815 6180142 152 18,01864

26T15:06:38Z 141 55,72743 13,47924 15,77506 2021-02- 1353696 6180032 153 161,9914

26T15:09:07Z 142 55,53246 12,92323 31,49421 2021-03- 1317877 6159652 154 41211,91

02T08:24:12Z 143 55,5325 12,92882 24,37577 2021-03- 1318230 6159641 155 353,2846

02T08:31:24Z 144 55,5327 12,93112 22,76454 2021-03- 1318376 6159657 156 147,1456

02T08:34:09Z 145 55,5435 12,93108 10,6062 2021-03- 1318423 6160859 157 1202,751

02T09:02:07Z 146 55,54301 12,93055 8,893089 2021-03- 1318387 6160807 158 63,35488

02T09:04:31Z 147 55,54259 12,93013 8,087837 2021-03- 1318359 6160761 159 54,03847

02T09:05:41Z 150 55,54409 12,99075 27,29243 2021-03- 1322191 6160771 162 3831,377

02T09:43:59Z 151 55,54486 12,99207 26,07276 2021-03- 1322277 6160853 163 119,0739

02T09:45:59Z 152 55,54556 12,99326 27,64315 2021-03- 1322355 6160928 164 108,2825

02T09:47:59Z 153 55,50061 12,9823 8,274932 2021-03- 1321460 6155954 165 5053,454

02T10:24:59Z 154 55,50095 12,9809 7,315767 2021-03- 1321374 6155996 166 95,95348 02T10:27:38Z

155 55,50144 12,97955 8,630884 2021-03- 1321291 6156054 167 101,4885

02T10:29:37Z 156 55,46253 13,06135 20,44779 2021-03- 1326286 6151517 168 6748,333

02T11:51:27Z 157 55,46213 13,06051 19,73151 2021-03- 1326231 6151474 169 69,51484

02T11:53:18Z 158 55,46107 13,05796 17,15165 2021-03- 1326065 6151363 170 200,1152

02T11:57:08Z 159 55,42977 12,97921 -1,82875 2021-03- 1320944 6148079 171 6082,691

02T13:55:57Z 160 55,43077 12,97832 -4,37262 2021-03- 1320893 6148193 172 124,987

02T13:59:42Z 161 55,43102 12,97738 -2,37843 2021-03- 1320834 6148224 173 65,8751

02T14:00:58Z 162 55,40234 13,03276 1,768977 2021-03- 1324211 6144891 174 4744,602

02T14:56:49Z 163 55,39956 13,03372 0,949084 2021-03- 1324259 6144579 175 315,2495

02T15:12:51Z 164 55,39885 13,03428 1,755075 2021-03- 1324291 6144499 176 86,35982

02T15:15:13Z 165 55,40376 13,05631 8,24842 2021-03- 1325708 6144990 177 1499,712

02T15:37:07Z 166 55,40462 13,05532 6,152484 2021-03- 1325649 6145088 178 114,5884

02T15:39:46Z 167 55,4065 13,05338 4,926224 2021-03- 1325535 6145302 179 242,4525

02T15:43:56Z 168 55,52243 13,29369 51,66093 2021-03- 1341217 6157627 180 19946,01

04T10:57:37Z 169 55,52247 13,29349 53,20359 2021-03- 1341204 6157631 181 13,33873

04T10:59:31Z 170 55,52289 13,29179 47,76064 2021-03- 1341099 6157682 182 117,2565

04T11:01:53Z 171 55,49429 13,26055 52,57296 2021-03- 1339010 6154571 183 3746,974

04T11:29:23Z 172 55,49524 13,262 49,65536 2021-03- 1339105 6154673 184 139,729

04T11:32:53Z 173 55,49777 13,26548 48,77102 2021-03- 1339335 6154947 185 357,5802

04T11:38:05Z 174 55,38689 13,45963 12,42018 2021-03- 1351183 6142177 186 17420,32 04T14:29:22Z

175 55,38635 13,45977 10,56518 2021-03- 1351190 6142116 187 60,97763

04T14:30:48Z 176 55,38552 13,45999 8,57732 2021-03- 1351201 6142023 188 93,54472

04T14:32:24Z 177 55,43476 13,47523 36,22652 2021-03- 1352350 6147470 189 5567,253

04T14:57:20Z 178 55,43412 13,47546 31,31633 2021-03- 1352362 6147399 190 72,42417

04T14:58:54Z 179 55,43346 13,47584 30,16602 2021-03- 1352384 6147324 191 77,94601

04T15:00:43Z 180 55,44483 13,47683 43,18819 2021-03- 1352489 6148587 192 1267,376

04T15:12:39Z 181 55,44335 13,47342 49,18916 2021-03- 1352267 6148430 193 271,5198

04T15:16:47Z 182 55,44285 13,47276 50,54009 2021-03- 1352224 6148375 194 69,97553

04T15:17:54Z 183 55,42838 13,42979 41,96384 2021-03- 1349451 6146858 195 3161,373

04T15:40:05Z 184 55,42833 13,43027 42,35359 2021-03- 1349481 6146850 196 30,75857

04T15:41:56Z 185 55,42806 13,43163 40,4268 2021-03- 1349566 6146818 197 91,11164

04T15:43:39Z 186 55,4328 13,36876 49,07674 2021-03- 1345606 6147483 198 4015,442

04T16:06:58Z 187 55,43209 13,37014 45,18679 2021-03- 1345690 6147401 199 117,5347

04T16:10:10Z 188 55,43131 13,37171 43,90336 2021-03- 1345787 6147310 200 132,5174

04T16:12:23Z 189 55,76792 12,96456 -0,33971 2021-03- 1321559 6185744 201 45432,59

05T10:04:25Z 190 55,76768 12,96778 0,00834 2021-03- 1321761 6185710 202 204,4692

05T10:14:39Z 191 55,76756 12,96938 0,584319 2021-03- 1321860 6185692 203 100,8417

05T10:16:39Z 192 55,76671 12,95858 2,080823 2021-03- 1321179 6185625 204 684,5827

05T10:37:08Z 193 55,76662 12,95965 -0,24524 2021-03- 1321246 6185612 205 68,01303

05T10:39:46Z 194 55,76654 12,96072 -0,37089 2021-03- 1321313 6185600 206 67,95262 05T10:41:49Z

195 55,70862 13,04218 -1,07545 2021-03- 1326165 6178949 207 8233,715

05T11:55:39Z 196 55,70737 13,04269 -1,12363 2021-03- 1326192 6178808 208 143,3028

05T12:00:25Z 197 55,70674 13,04292 -1,72129 2021-03- 1326203 6178738 209 70,93278

05T12:02:09Z 198 55,72467 13,03697 6,322813 2021-03- 1325910 6180747 210 2030,674

05T13:19:57Z 199 55,72591 13,03658 5,418687 2021-03- 1325890 6180887 2111 140,7156

05T13:24:07Z 200 55,72694 13,03618 6,625188 2021-03- 1325870 6181002 2121 116,893

05T13:26:27Z 201 55,72077 13,06682 2,789028 2021-03- 1327767 6180239 213 2044,828

05T14:32:01Z 202 55,72157 13,06856 0,366501 2021-03- 1327880 6180324 214 141,4826

05T14:34:32Z 203 55,72253 13,0701 0,51335 2021-03- 1327981 6180426 215 143,6904

05T14:37:35Z 204 55,68298 13,09338 2,909491 2021-03- 1329270 6175968 216 4640,789

05T15:48:52Z 205 55,68296 13,09354 3,031807 2021-03- 1329280 6175965 217 10,74911

05T15:50:48Z 206 55,68359 13,09447 1,781769 2021-03- 1329342 6176033 218 91,61777

05T15:53:48Z 207 55,68376 13,09474 0,700844 2021-03- 1329359 6176051 219 25,39313

05T15:54:36Z 208 55,89023 12,87933 9,02246 2021-03- 1316789 6199577 220 26673,02

08T15:43:42Z 209 55,89023 12,87932 9,460926 2021-03- 1316789 6199577 2211 0,218839

08T15:43:59Z 210 55,89003 12,87689 9,389332 2021-03- 1316636 6199561 222 154,1116

08T15:46:40Z 211 55,8897 12,87339 10,34206 2021-03- 1316415 6199534 223 222,1566

08T15:50:05Z 212 55,76736 13,4017 78,43701 2021-03- 1348981 6184642 224 35808,87

09T08:59:20Z 213 55,76731 13,40198 77,10806 2021-03- 1348998 6184636 225 18,44202

09T09:00:25Z 214 55,76689 13,40491 78,35535 2021-03- 1349181 6184583 226 189,8505 09T09:03:08Z

215 55,74482 13,40925 26,14507 2021-03- 1349368 6182118 227 2472,704

09T09:29:44Z 216 55,74402 13,41022 25,00465 2021-03- 1349426 6182026 228 107,8591

09T09:32:50Z 217 55,74377 13,41092 25,81076 2021-03- 1349469 6181997 229 52,16226

09T09:33:55Z 218 55,78968 13,36491 53,71539 2021-03- 1346761 6187207 230 5872,094

09T10:11:10Z 219 55,7905 13,36514 53,11693 2021-03- 1346778 6187298 231 92,37811

09T10:14:02Z 220 55,79139 13,36532 56,50737 2021-03- 1346793 6187397 232 99,65369

09T10:34:02Z 221 55,77958 13,36089 55,54234 2021-03- 1346469 6186092 233 1344,095

09T10:58:04Z 222 55,7796 13,36222 55,72376 2021-03- 1346552 6186092 234 83,32364

09T11:00:24Z 223 55,77917 13,36264 57,32972 2021-03- 1346577 6186042 235 55,24869

09T11:02:25Z 224 55,68722 13,23886 24,69626 2021-03- 1338434 6176091 236 12858,27

09T12:39:39Z 225 55,68731 13,23681 22,57012 2021-03- 1338306 6176106 237 129,5378

09T12:43:39Z 226 55,68732 13,23607 23,10187 2021-03- 1338259 6176108 238 46,80783

09T12:45:30Z 227 55,76926 13,3202 32,82232 2021-03- 1343876 6185034 239 10545,9

09T14:06:15Z 228 55,76921 13,31973 28,06417 2021-03- 1343846 6185030 240 29,90678

09T14:07:44Z 229 55,76892 13,31641 18,97432 2021-03- 1343637 6185006 241 210,8949

09T14:10:05Z 230 55,6876 13,10522 2,695009 2021-03- 1330035 6176453 242 16067,2

09T17:03:57Z 231 55,68695 13,10648 3,665304 2021-03- 1330111 6176378 243 107,6943

09T17:06:56Z 232 55,68671 13,1078 2,786841 2021-03- 1330193 6176348 244 86,9232

09T17:08:25Z 233 55,54474 12,98526 28,82461 2021-03- 1321847 6160857 245 17595,98

10T08:42:43Z 234 55,5447 12,9846 26,70537 2021-03- 1321805 6160855 246 42,00546 10T08:44:48Z

235 55,54459 12,98336 25,50761 2021-03- 1321727 6160845 247 79,2116

10T08:46:14Z 236 55,5035 12,94281 2,919195 2021-03- 1318979 6156378 248 5244,341

10T09:38:02Z 237 55,50345 12,94315 2,489479 2021-03- 1319001 6156371 249 22,34894

10T09:39:12Z 238 55,50308 12,94447 6,981837 2021-03- 1319083 6156327 250 93,34122

10T09:40:45Z 239 55,52033 12,94165 11,06167 2021-03- 1318984 6158254 251 1929,636

10T10:03:10Z 240 55,52063 12,94144 8,249506 2021-03- 1318971 6158288 252 36,14364

10T10:04:49Z 241 55,52145 12,94073 8,676559 2021-03- 1318931 6158380 253 101,358

10T10:06:28Z 242 55,51871 12,94638 8,260294 2021-03- 1319275 6158062 254 469,2327

10T10:20:00Z 243 55,51834 12,94542 7,805914 2021-03- 1319212 6158023 255 73,51557

10T10:21:39Z 244 55,51785 12,94416 5,378344 2021-03- 1319131 6157971 256 96,34289

10T10:23:06Z 245 55,5184 12,95583 9,78507 2021-03- 1319870 6158002 257 740,1331

10T10:50:05Z 246 55,51852 12,95685 7,844538 2021-03- 1319935 6158013 258 65,56946

10T10:52:27Z 247 55,51857 12,95941 10,28617 2021-03- 1320097 6158011 259 162,087

10T10:55:25Z 248 55,5129 13,13282 39,925 2021-03- 1331020 6156945 260 10975,03

10T13:39:20Z 249 55,51276 13,13262 39,33396 2021-03- 1331007 6156930 261 20,01848

10T13:40:46Z 250 55,51188 13,13083 34,96053 2021-03- 1330890 6156837 262 149,4683

10T13:43:29Z 251 55,50775 13,11411 34,50716 2021-03- 1329817 6156419 263 1151,984

10T13:58:09Z 252 55,50674 13,11569 34,76255 2021-03- 1329912 6156302 264 150,9004

10T14:01:09Z 253 55,50604 13,1168 33,01004 2021-03- 1329979 6156221 265 104,95

10T14:02:52Z 254 55,45063 13,12297 41,01243 2021-03- 1330131 6150041 266 6182,076 10T14:55:25Z

255 55,4512 13,12369 37,69982 2021-03- 1330178 6150102 267 77,81162

10T15:01:41Z 256 55,45188 13,12478 36,23451 2021-03- 1330250 6150175 268 101,9771

10T15:04:05Z 257 55,49991 12,96387 7,728027 2021-03- 1320293 6155924 269 11497,2

10T15:46:33Z 258 55,49935 12,96374 6,134318 2021-03- 1320282 6155862 270 62,47564

10T15:48:23Z 259 55,49798 12,96337 6,580555 2021-03- 1320253 6155710 271 154,6571

10T15:50:31Z 260 55,50786 12,93924 0,982719 2021-03- 1318774 6156872 272 1880,672

10T16:11:00Z 261 55,50816 12,93998 4,59441 2021-03- 1318822 6156904 273 57,43897

10T16:12:59Z 262 55,50879 12,94141 6,11795 2021-03- 1318915 6156970 274 114,0064

10T16:14:42Z 263 55,65351 13,05505 18,92722 2021-03- 1326730 6172784 275 17639,51

12T08:23:50Z 264 55,65325 13,0553 17,09197 2021-03- 1326745 6172754 276 33,11709

12T08:25:01Z 265 55,65262 13,05547 17,16029 2021-03- 1326753 6172684 277 70,81137

12T08:27:03Z 266 55,66312 13,07341 9,133387 2021-03- 1327927 6173807 278 1625,608

12T08:45:58Z 267 55,66296 13,07275 10,5332 2021-03- 1327885 6173791 279 44,91544

12T08:47:22Z 268 55,66278 13,07046 7,393555 2021-03- 1327741 6173777 280 145,3562

12T08:50:05Z 269 55,69251 13,08952 2,417543 2021-03- 1329069 6177038 281 3520,687

12T09:22:23Z 270 55,69298 13,08989 1,001623 2021-03- 1329095 6177089 282 57,3582

12T09:23:51Z 271 55,69374 13,09055 1,443775 2021-03- 1329139 6177173 283 94,97253

12T09:26:03Z 272 55,70396 13,06014 1,732274 2021-03- 1327273 6178384 284 2225,127

12T09:59:27Z 273 55,70387 13,06024 1,287242 2021-03- 1327279 6178375 285 11,19953

12T10:00:10Z 274 55,7028 13,06212 -2,36862 2021-03- 1327393 6178251 286 167,7098 12T10:03:32Z

275 55,73613 13,05139 5,150364 2021-03- 1326866 6181986 288 3771,622

12T10:33:57Z 276 55,73638 13,05154 4,694739 2021-03- 1326877 6182014 289 30,31629

12T10:35:34Z 277 55,73728 13,05325 4,289729 2021-03- 1326988 6182110 290 146,4797

12T10:40:32Z 278 55,77169 12,93142 1,887639 2021-03- 1319498 6186250 291 8557,77

12T12:26:57Z 279 55,77064 12,93031 2,279242 2021-03- 1319424 6186136 292 136,447

12T12:29:44Z 280 55,76917 12,92867 5,179914 2021-03- 1319314 6185976 293 194,0369

12T12:33:11Z 281 55,7666 12,95864 4,750278 2021-03- 1321182 6185613 294 1903,291

12T13:20:31Z 282 55,76679 12,95882 4,039732 2021-03- 1321194 6185633 295 24,07211

12T13:21:35Z 283 55,76749 12,95992 3,932522 2021-03- 1321267 6185709 296 104,3563

12T13:23:49Z 284 55,76515 12,98271 5,21518 2021-03- 1322686 6185390 297 1454,601

12T13:40:03Z 285 55,7653 12,98268 5,486272 2021-03- 1322684 6185406 298 16,09383

12T13:41:06Z 286 55,7662 12,98258 2,477788 2021-03- 1322683 6185507 299 101,0572

12T13:42:55Z 287 55,75433 13,02323 -0,17489 2021-03- 1325179 6184083 300 2874,199

12T14:36:14Z 288 55,75541 13,02213 -1,91805 2021-03- 1325115 6184206 301 138,8321

12T14:39:58Z 289 55,75612 13,0222 -3,21658 2021-03- 1325123 6184284 302 78,87039

12T14:42:09Z 290 55,78653 13,1551 20,03942 2021-03- 1333592 6187341 303 9003,773

12T15:25:44Z 291 55,78719 13,15296 18,71335 2021-03- 1333461 6187419 304 152,7009

12T15:28:47Z 292 55,78773 13,15171 18,74139 2021-03- 1333385 6187482 305 98,8328

12T15:31:05Z 293 55,76926 13,17778 28,99952 2021-03- 1334941 6185365 306 2627,747

12T15:53:09Z 294 55,76953 13,17817 28,63389 2021-03- 1334967 6185394 307 39,02359 12T15:54:50Z

295 55,77027 13,17965 27,6846 2021-03- 1335063 6185473 308 124,1325

12T15:56:42Z 296 55,50542 13,83722 45,54527 2021-03- 1375479 6154625 309 50843,2

15T09:07:45Z 297 55,50601 13,83645 40,32284 2021-03- 1375432 6154692 310 82,07285

15T09:10:31Z 298 55,50672 13,83474 43,226 2021-03- 1375326 6154774 311 133,9686

15T09:12:55Z 299 55,44441 13,90237 -0,65159 2021-03- 1379408 6147720 313 8149,872

15T09:56:50Z 300 55,4446 13,90277 -2,07253 2021-03- 1379434 6147741 314 33,63782

15T09:59:05Z 301 55,44486 13,90335 -2,61022 2021-03- 1379471 6147768 315 45,91839

15T10:00:21Z 302 55,44696 13,80454 41,34512 2021-03- 1373227 6148179 316 6257,816

15T10:54:30Z 303 55,4469 13,80487 37,92692 2021-03- 1373248 6148171 317 22,34175

15T10:55:52Z 304 55,4467 13,80602 30,53236 2021-03- 1373319 6148147 318 75,49602

15T10:57:48Z 305 55,44872 13,72913 34,29351 2021-03- 1368462 6148514 319 4871,063

15T11:25:26Z 306 55,44865 13,72908 32,87208 2021-03- 1368459 6148506 320 8,003971

15T11:27:02Z 307 55,44812 13,72844 26,6957 2021-03- 1368417 6148449 321 71,17338

15T11:28:16Z 308 55,4283 13,66346 25,82541 2021-03- 1364238 6146367 322 4668,051

15T11:51:36Z 309 55,42834 13,66292 25,50002 2021-03- 1364204 6146373 323 34,58568

15T11:52:56Z 310 55,42859 13,66134 22,23443 2021-03- 1364105 6146404 324 103,9679

15T11:54:42Z 311 55,44023 13,62866 39,73042 2021-03- 1362077 6147764 325 2441,955

15T12:17:06Z 312 55,43981 13,62873 38,9232 2021-03- 1362080 6147717 326 47,10879

15T12:18:39Z 313 55,43881 13,62871 37,39048 2021-03- 1362075 6147606 327 111,8053

15T12:20:34Z 314 55,42209 13,54627 20,24775 2021-03- 1356799 6145912 328 5541,703 15T13:03:32Z

315 55,42233 13,54787 18,47555 2021-03- 1356901 6145935 329 105,099

15T13:05:59Z 316 55,4231 13,55009 16,73498 2021-03- 1357045 6146017 330 164,7385

15T13:08:44Z 317 55,42208 13,54621 18,22368 2021-03- 1356795 6145910 331 271,131

15T13:20:41Z 318 55,42224 13,545 17,18327 2021-03- 1356719 6145931 332 78,82174

15T13:22:55Z 319 55,42271 13,54335 15,82754 2021-03- 1356616 6145986 333 116,9185

15T13:24:54Z 320 55,45625 13,17015 31,98522 2021-03- 1333138 6150551 334 23918,23

15T14:35:52Z 321 55,45582 13,17038 30,39816 2021-03- 1333151 6150503 335 49,63313

15T14:37:51Z 322 55,45499 13,17093 29,12004 2021-03- 1333182 6150410 336 98,90764

15T14:39:33Z 323 55,55639 13,25546 44,70738 2021-03- 1338942 6161493 337 12490,99

16T09:00:35Z 324 55,55656 13,25513 46,34395 2021-03- 1338922 6161513 338 28,49875

16T09:03:31Z 325 55,55727 13,25329 47,01405 2021-03- 1338809 6161596 339 140,6684

16T09:06:10Z 326 55,58156 13,26768 35,78939 2021-03- 1339815 6164267 340 2853,66

16T09:30:01Z 327 55,58152 13,26841 35,44519 2021-03- 1339861 6164260 341 46,72381

16T09:31:21Z 328 55,58135 13,2698 37,61789 2021-03- 1339948 6164238 342 89,27724

16T09:33:22Z 329 55,57468 13,2989 39,19137 2021-03- 1341755 6163430 343 1980,242

16T09:53:49Z 330 55,57402 13,2991 38,55108 2021-03- 1341765 6163355 344 74,9

16T09:55:32Z 331 55,57316 13,29921 36,61025 2021-03- 1341769 6163259 345 96,13147

16T09:57:16Z 332 55,58341 13,3472 40,63937 2021-03- 1344835 6164292 346 3235,722

16T10:32:43Z 333 55,58363 13,3457 38,46458 2021-03- 1344741 6164319 347 97,9044

16T10:36:53Z 334 55,58387 13,34513 36,54791 2021-03- 1344707 6164347 348 44,69517 16T10:38:51Z

335 55,57767 13,35956 50,02386 2021-03- 1345592 6163626 349 1141,784

16T11:01:54Z 336 55,57729 13,35933 48,57607 2021-03- 1345575 6163583 350 45,19312

16T11:03:31Z 337 55,57685 13,35917 46,80976 2021-03- 1345564 6163535 351 49,60258

16T11:04:33Z 338 55,60444 13,38554 29,07272 2021-03- 1347334 6166547 352 3493,104

16T11:27:04Z 339 55,60468 13,38564 27,99246 2021-03- 1347341 6166573 353 27,3553

16T11:28:36Z 340 55,60535 13,38592 28,89661 2021-03- 1347361 6166648 354 77,23117

16T11:29:52Z 341 55,63928 13,42054 60,6204 2021-03- 1349672 6170348 355 4362,698

16T11:55:20Z 342 55,63842 13,42022 59,65858 2021-03- 1349648 6170253 356 97,86078

16T11:57:32Z 343 55,63724 13,41969 59,03396 2021-03- 1349611 6170122 357 135,9967

16T11:59:40Z 344 55,62904 13,33137 27,42321 2021-03- 1344019 6169405 358 5637,985

16T12:50:21Z 345 55,6285 13,33331 26,11838 2021-03- 1344139 6169340 359 136,5516

16T12:53:08Z 346 55,62804 13,33527 27,23727 2021-03- 1344260 6169285 360 133,5265

16T12:55:03Z 347 55,54434 12,93107 8,620944 2021-03- 1318427 6160953 361 27144

17T08:33:58Z 348 55,54427 12,92966 9,534369 2021-03- 1318337 6160949 362 89,69073

17T08:37:42Z 349 55,54411 12,92928 8,233639 2021-03- 1318312 6160932 363 30,00779

17T08:38:39Z 350 55,51621 13,1533 38,49125 2021-03- 1332327 6157264 364 14487,13

17T09:31:06Z 351 55,5168 13,15324 32,10976 2021-03- 1332326 6157330 365 66,26593

17T09:32:27Z 352 55,49285 13,1398 40,22172 2021-03- 1331375 6154698 366 2799,17

17T09:58:54Z 353 55,49241 13,13999 36,42191 2021-03- 1331385 6154647 367 51,36718

17T10:00:07Z 354 55,4912 13,14056 35,98028 2021-03- 1331416 6154511 368 139,3508 17T10:02:08Z

355 55,40371 13,02023 2,56894 2021-03- 1323424 6145075 369 12366,01

17T11:16:07Z 356 55,40276 13,02209 1,030315 2021-03- 1323537 6144965 370 158,2854

17T11:19:17Z 357 55,40178 13,02422 -0,60515 2021-03- 1323668 6144850 371 173,5849

17T11:21:55Z 358 55,385 13,12538 1,744745 2021-03- 1330001 6142732 372 6678,249

17T12:02:03Z 359 55,38617 13,12451 0,395987 2021-03- 1329951 6142864 373 141,1015

17T12:04:36Z 360 55,38682 13,12399 -1,11195 2021-03- 1329921 6142937 374 79,70536

17T12:14:59Z 361 55,43059 13,0958 23,51628 2021-03- 1328325 6147877 375 5190,776

17T13:14:02Z 362 55,43065 13,09644 24,53103 2021-03- 1328366 6147882 376 41,4035

17T13:15:37Z 363 55,43062 13,09832 25,3717 2021-03- 1328485 6147875 377 118,7606

17T13:17:31Z 364 55,5 12,94714 -2,03972 2021-03- 1319237 6155977 378 12295,05

17T14:20:47Z 365 55,4995 12,94561 -2,92949 2021-03- 1319138 6155926 379 110,9973

17T14:26:41Z 366 55,49941 12,94479 -4,15089 2021-03- 1319086 6155918 380 53,31151

17T14:27:49Z 367 55,50449 12,94201 -1,45354 2021-03- 1318933 6156490 381 591,7081

17T14:58:38Z 368 55,5044 12,94296 -1,50497 2021-03- 1318993 6156478 382 60,83037

17T15:01:11Z 369 55,50432 12,94416 -0,9947 2021-03- 1319069 6156466 383 76,58716

17T15:02:33Z 370 55,53243 12,9231 2,829058 2021-03- 1317868 6159649 384 3401,822

17T16:04:23Z 371 55,53215 12,92466 1,601025 2021-03- 1317966 6159613 385 103,2843

17T16:06:31Z 372 55,53197 12,92527 1,936439 2021-03- 1318004 6159592 386 43,72406

17T16:08:01Z 373 55,6564 13,06192 11,17529 2021-03- 1327175 6173088 387 16317,84

18T08:54:44Z 374 55,65529 13,06167 11,56152 2021-03- 1327155 6172965 388 124,8925 18T08:57:04Z

375 55,65379 13,06151 11,61173 2021-03- 1327138 6172798 389 167,812

18T08:59:24Z 376 55,75633 13,01975 2,639942 2021-03- 1324970 6184314 390 11718,2

18T10:10:35Z 377 55,75617 13,02171 3,738016 2021-03- 1325092 6184291 391 124,6238

18T10:13:17Z 378 55,75612 13,02237 1,705357 2021-03- 1325133 6184284 392 41,41552

18T10:15:03Z 379 55,93341 13,11006 68,67542 2021-03- 1331405 6203795 393 20493,73

18T11:51:47Z 380 55,93308 13,10977 70,7884 2021-03- 1331386 6203759 394 40,87895

18T11:53:08Z 381 55,93267 13,10943 66,53445 2021-03- 1331363 6203714 395 50,41336

18T11:54:08Z 382 55,77967 12,93478 0,384895 2021-03- 1319746 6187129 396 20248,79

18T14:07:26Z 383 55,7799 12,93538 2,025318 2021-03- 1319785 6187153 397 45,56479

18T14:08:43Z 384 55,78026 12,93634 5,381477 2021-03- 1319846 6187191 398 72,51169

18T14:10:01Z 385 55,76274 12,92092 5,044388 2021-03- 1318798 6185282 399 2177,778

18T14:27:54Z 386 55,76401 12,91805 3,062504 2021-03- 1318624 6185430 400 228,6272

18T14:31:31Z 387 55,76455 12,91675 3,019287 2021-03- 1318545 6185494 401 102,1891

18T14:35:05Z 388 55,7666 12,95867 4,074963 2021-03- 1321184 6185613 402 2642,37

18T15:05:30Z 389 55,76644 12,95904 4,887672 2021-03- 1321207 6185593 403 29,55802

18T15:07:57Z 390 55,766 12,96 1,756588 2021-03- 1321264 6185543 404 76,94796

18T15:09:20Z 391 55,77072 13,05658 9,560856 2021-03- 1327344 6185823 405 6086,264

18T16:01:05Z 392 55,77058 13,05683 5,563328 2021-03- 1327359 6185807 406 21,99778

18T16:02:36Z 393 55,76072 13,09729 27,69247 2021-03- 1329854 6184609 407 2767,433

18T16:27:07Z 394 55,76002 13,09717 25,061 2021-03- 1329844 6184531 408 78,53237 18T16:30:28Z

395 55,75921 13,09707 23,79665 2021-03- 1329834 6184442 409 90,40432

18T16:32:08Z 396 55,5491 13,07087 44,67797 2021-03- 1327267 6161126 410 23456,51

19T08:18:30Z 397 55,54856 13,07101 42,87995 2021-03- 1327274 6161066 411 60,56443

19T08:22:22Z 398 55,54807 13,07131 44,33549 2021-03- 1327291 6161010 412 58,0146

19T08:23:11Z 399 55,52187 13,08295 45,48644 2021-03- 1327910 6158066 413 3009,121

19T08:46:01Z 400 55,52157 13,08262 41,15174 2021-03- 1327888 6158034 414 38,60371

19T08:48:14Z 401 55,52027 13,0806 39,71286 2021-03- 1327755 6157894 415 193,4442

19T08:51:01Z 402 55,48545 13,12384 40,01007 2021-03- 1330335 6153913 416 4744,22

19T09:26:40Z 403 55,48525 13,12301 38,22682 2021-03- 1330282 6153892 417 56,66686

19T09:28:12Z 404 55,48468 13,12059 32,99753 2021-03- 1330126 6153835 418 165,9743

19T09:30:31Z 405 55,34997 13,38531 1,65882 2021-03- 1346332 6138229 419 22498,42

19T11:08:31Z 406 55,35101 13,38704 -0,42658 2021-03- 1346446 6138341 420 159,9517

19T11:11:37Z 407 55,3514 13,3876 0 2021-03- 1346482 6138383 421 55,13462

19T11:14:02Z 408 55,45921 13,47166 42,00013 2021-03- 1352215 6150199 422 13133,51

19T12:35:34Z 409 55,45924 13,47168 42,48385 2021-03- 1352217 6150202 423 3,671989

19T12:39:10Z 410 55,46002 13,47119 40,08896 2021-03- 1352189 6150290 424 92,21503

19T12:41:33Z 411 55,48652 13,47579 48,18353 2021-03- 1352579 6153229 425 2965,41

19T13:15:22Z 412 55,48645 13,47554 46,75659 2021-03- 1352562 6153221 426 18,08989

19T13:16:45Z 413 55,48592 13,47457 46,35252 2021-03- 1352500 6153165 427 84,65149

19T13:18:16Z 414 55,50729 13,49185 55,45178 2021-03- 1353671 6155506 428 2618,35 19T14:13:44Z

415 55,50685 13,49183 54,1338 2021-03- 1353667 6155458 429 49,03109

19T14:15:13Z 416 55,50633 13,49182 53,04097 2021-03- 1353665 6155400 430 57,68163

19T14:16:21Z 417 55,51755 13,51656 59,60595 2021-03- 1355269 6156597 431 2001,439

19T14:46:07Z 418 55,51791 13,51554 56,58135 2021-03- 1355206 6156639 432 76,26804

19T14:49:18Z 419 55,51821 13,51449 54,75571 2021-03- 1355141 6156674 433 73,92389

19T14:51:45Z 420 55,52527 13,44283 72,70718 2021-03- 1350643 6157612 434 4594,852

19T15:20:42Z 421 55,52574 13,44491 68,4007 2021-03- 1350776 6157660 435 141,3599

19T15:23:12Z 422 55,52585 13,44596 68,59145 2021-03- 1350843 6157670 436 67,55019

19T15:24:38Z 423 55,76965 13,31967 -4,93901 2021-03- 1343844 6185079 437 28288,76

21T09:38:21Z 424 55,76961 13,3196 -6,38553 2021-03- 1343839 6185075 438 6,526876

21T09:39:44Z 425 55,76924 13,31716 -13,0195 2021-03- 1343685 6185039 439 158,5811

21T09:42:37Z 426 55,72248 13,50923 28,49767 2021-03- 1355562 6179418 440 13140,14

21T10:38:11Z 427 55,72276 13,50805 29,21067 2021-03- 1355489 6179452 441 80,28909

21T10:39:59Z 428 55,72296 13,50709 29,16967 2021-03- 1355429 6179477 442 64,51319

21T10:41:17Z 429 55,67948 13,49834 19,01077 2021-03- 1354718 6174656 443 4873,133

21T12:10:36Z 430 55,68025 13,49802 19,8106 2021-03- 1354701 6174742 444 88,3111

21T12:12:35Z 431 55,68065 13,49794 18,49647 2021-03- 1354697 6174787 445 44,94783

21T12:14:03Z 432 55,72324 13,5072 31,8952 2021-03- 1355437 6179507 446 4777,34

21T13:04:58Z 433 55,72265 13,50773 30,73897 2021-03- 1355468 6179440 447 73,8314

21T13:07:02Z 434 55,72207 13,50833 26,4206 2021-03- 1355503 6179375 449 73,95582 21T13:11:27Z

435 55,64579 13,31632 21,13865 2021-03- 1343138 6171303 450 14766,96

21T13:58:04Z 436 55,64751 13,31677 18,85397 2021-03- 1343173 6171493 451 193,1149

21T14:01:12Z 437 55,64911 13,31697 20,67863 2021-03- 1343192 6171670 452 178,6075

21T14:04:37Z 438 55,62862 13,33141 28,21481 2021-03- 1344019 6169358 453 2455,973

21T14:27:36Z 439 55,6286 13,3319 28,09393 2021-03- 1344050 6169354 454 31,46769

21T14:29:05Z 440 55,62856 13,33295 27,0866 2021-03- 1344116 6169348 455 65,87262

21T14:30:47Z 441 55,52159 12,93791 54,24532 2021-03- 1318753 6158404 456 27623,69

22T09:02:50Z 442 55,52154 12,93957 50,64675 2021-03- 1318858 6158393 457 105,1666

22T09:05:17Z 443 55,52157 12,94078 49,76487 2021-03- 1318935 6158394 458 76,79001

22T09:06:37Z 444 55,39209 13,08835 7,362122 2021-03- 1327686 6143611 459 17178,94

22T10:15:56Z 445 55,39047 13,09141 4,735561 2021-03- 1327873 6143424 460 264,5239

22T10:21:12Z 446 55,39011 13,09163 6,010231 2021-03- 1327886 6143383 461 42,815

22T10:23:13Z 447 55,52025 13,09421 49,77419 2021-03- 1328614 6157858 462 14493,42

22T11:37:42Z 448 55,51916 13,09483 46,16747 2021-03- 1328649 6157735 463 128,0778

22T11:40:16Z 449 55,51875 13,09505 47,51655 2021-03- 1328661 6157689 464 47,63376

22T11:41:35Z 450 55,46656 13,14196 35,94668 2021-03- 1331399 6151767 465 6524,115

22T12:40:45Z 451 55,46629 13,14231 35,19114 2021-03- 1331421 6151736 466 38,0751

22T12:42:10Z 452 55,4664 13,14279 34,77388 2021-03- 1331451 6151747 467 32,51932

22T12:43:31Z 453 55,46675 13,14337 33,659 2021-03- 1331489 6151785 468 53,64622

22T12:44:53Z 454 55,5202 13,08515 36,4001 2021-03- 1328042 6157874 469 6997,609 22T13:07:49Z

455 55,52005 13,08524 36,38959 2021-03- 1328047 6157858 470 17,5643

22T13:08:43Z 456 55,51958 13,0855 38,35184 2021-03- 1328061 6157805 471 54,97881

22T13:09:35Z 457 55,53758 13,05471 31,09154 2021-03- 1326197 6159885 472 2793,296

22T14:10:40Z 458 55,53811 13,05465 30,84057 2021-03- 1326196 6159943 473 58,70427

22T14:12:38Z 459 55,53855 13,05457 32,53769 2021-03- 1326193 6159993 474 49,36025

22T14:13:57Z 460 55,5268 13,04517 33,12975 2021-03- 1325547 6158708 475 1437,118

22T14:29:20Z 461 55,52631 13,04478 30,43565 2021-03- 1325520 6158655 476 59,85107

22T14:31:44Z 462 55,52587 13,04441 30,79541 2021-03- 1325495 6158607 477 54,02915

22T14:32:55Z 463 55,50138 12,94531 3,251446 2021-03- 1319128 6156135 478 6830,485

22T14:57:38Z 464 55,50138 12,9463 4,513065 2021-03- 1319190 6156133 479 62,77291

22T14:59:19Z 465 55,50136 12,94699 4,101017 2021-03- 1319233 6156129 480 43,23294

22T15:00:21Z 466 55,50638 12,94032 1,114475 2021-03- 1318836 6156705 481 699,7541

22T15:18:24Z 467 55,50687 12,94187 2,999321 2021-03- 1318935 6156756 482 112,0235

22T15:22:15Z 468 55,50749 12,94345 3,618034 2021-03- 1319038 6156821 483 121,4342

22T15:24:27Z 469 55,61548 13,09994 11,77482 2021-03- 1329389 6168440 4831 15561,24

23T08:29:07Z 470 55,61545 13,09995 12,50293 2021-03- 1329390 6168437 484 2,735656

23T08:31:27Z 471 55,6163 13,09967 7,975268 2021-03- 1329376 6168532 485 95,91648

23T08:33:35Z 472 55,61663 13,09948 7,382671 2021-03- 1329365 6168569 486 38,46637

23T08:34:48Z 473 55,7448 13,14076 22,97877 2021-03- 1332514 6182732 487 14508,17

23T10:29:36Z 474 55,74498 13,13964 21,08789 2021-03- 1332444 6182754 488 73,12492 23T10:31:44Z

475 55,74513 13,13868 22,3393 2021-03- 1332385 6182774 489 62,45517

23T10:32:51Z 476 55,71901 13,06066 9,370071 2021-03- 1327372 6180059 490 5700,896

23T11:21:49Z 477 55,7186 13,06172 6,377293 2021-03- 1327437 6180010 491 81,23662

23T11:23:51Z 478 55,71767 13,06371 1,838721 2021-03- 1327558 6179902 492 162,0857

23T11:33:24Z 479 55,70995 13,0539 2,026811 2021-03- 1326908 6179068 493 1057,526

23T12:31:08Z 480 55,70952 13,05391 2,696233 2021-03- 1326907 6179020 494 47,8937

23T12:33:03Z 481 55,70912 13,05408 0,589469 2021-03- 1326915 6178975 495 45,90259

23T12:34:10Z 482 55,70932 13,04588 -1,6985 2021-03- 1326401 6179017 496 516,5006

23T12:47:06Z 483 55,72367 13,03986 10,55884 2021-03- 1326087 6180629 497 1641,885

23T13:34:48Z 484 55,72439 13,03817 8,098229 2021-03- 1325983 6180713 498 132,8895

23T13:43:09Z 485 55,72546 13,03647 9,160866 2021-03- 1325881 6180836 499 160,2508

23T13:45:36Z 486 55,65718 13,06421 9,420291 2021-03- 1327322 6173169 500 7801,509

23T14:29:57Z 487 55,65795 13,06392 9,341206 2021-03- 1327308 6173255 501 87,46444

23T14:33:14Z 488 55,65862 13,06365 8,491909 2021-03- 1327294 6173330 502 76,57849

23T14:34:45Z 489 55,66409 13,09505 15,58217 2021-03- 1329293 6173861 503 2068,618

23T14:54:59Z 490 55,66337 13,09581 10,44834 2021-03- 1329338 6173780 504 93,04837

23T14:57:14Z 491 55,66311 13,09604 10,98442 2021-03- 1329351 6173750 505 32,64637

23T14:58:17Z 492 55,72874 13,03189 7,172091 2021-03- 1325608 6181213 506 8349,321

23T16:00:09Z 493 55,72815 13,02977 7,5872 2021-03- 1325473 6181153 507 148,5624

23T16:03:12Z 494 55,72786 13,02853 7,101553 2021-03- 1325393 6181123 508 84,85625 23T16:04:36Z

495 55,75041 13,00612 9,220432 2021-03- 1324088 6183690 509 2879,591

23T16:47:27Z 496 55,75046 13,00703 6,55769 2021-03- 1324145 6183693 510 57,86479

23T16:49:03Z 497 55,75049 13,00845 3,788406 2021-03- 1324234 6183692 511 88,74414

23T16:50:31Z 498 55,60001 13,00878 0 2021-03- 1323579 6166947

10T18:16:47Z